The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the
limitations which are existing nowadays. Although high performance ratio for video object detection and
tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to
propose a novel video object detection and tracking technique so as to minimize the computational
complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature
extraction, background subtraction and hole filling. Originally the video clip in the database is split into
frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this
stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified
region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract
the multi features from the segmented image and the background image, the feature value thus obtained
are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The
foreground image is then subjected to morphological operations of erosion and dilation so as to fill the
holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus
the moving object is tracked in this stage. This method will be employed in MATLAB platform and the
outcomes will be studied and compared with the existing techniques so as to reveal the performance of the
novel video object detection and tracking technique.
Real Time Detection of Moving Object Based on Fpgaiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
Objects detection and tracking using fast principle component purist and kalm...IJECEIAES
The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Regionconvolution neural networks (Fast- RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.
Real Time Detection of Moving Object Based on Fpgaiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
Objects detection and tracking using fast principle component purist and kalm...IJECEIAES
The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Regionconvolution neural networks (Fast- RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.
SENSITIVITY OF A VIDEO SURVEILLANCE SYSTEM BASED ON MOTION DETECTIONsipij
The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements.Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points.Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart
camera in industrial systems.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
Analysis of Human Behavior Based On Centroid and Treading TrackIJMER
Human body motion analysis is an important technology which modem bio-mechanics
combines with computer vision and has been widely used in intelligent control, human computer
interaction, motion analysis, and virtual reality and other fields. In which the moving human body
detection is the most important part of the human body motion analysis, the purpose is to detect the
moving human body with its behavior from the background image in video sequences, and for the follow-up treatment such as the target classification, the human body tracking and behavior understanding, its
effective detection plays a very important role
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
Robust Motion Detection and Tracking of Moving Objects using HOG Feature and ...CSCJournals
Detection and tracking of moving objects has gained significant importance due to intense technological progress in the field of computer science dealing with video surveillance systems. Human motion is generally nonlinear and non-Gaussian and thus many algorithms are not suitable for tracking. One of the applications to maintain universal security is crowd control. The main problem of video surveillance is continuous monitoring with regard to crime prevention. For security monitoring of live surveillance systems, target identification and tracking strategies can automatically send warnings to monitoring officers. In this paper, we propose a robust tracking of a specified person using the individuals' feature. The proposed method to determine automatic detection and tracking combines Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The Histogram oriented Gradient features are applied to single detection window for the identification of human area, after we use particle filters for robust specific people tracking using color and skin color based on the characteristics of a target individual. We have been improving the implementation, evaluation system of our proposed methods. In our systems, for experiments, we choose structured crowded scenes. From our experimental results, we have achieved high accuracy detection rates and robust motion tracking for specific targets.
A Robot Collision Avoidance Method Using Kinect and Global VisionTELKOMNIKA JOURNAL
This paper introduces a robot collision avoidance method using Kinect and global vision to
improve the industrial robot’s security. Global vision is installed above the robot, and a combination of the
background-difference method and the Otsu algorithm are used. Human skeleton detection is then
introduced to detect the location information of the human body. The collided objects are classified into
nonhuman and human obstacle which is further categorized into the human head and non-head areas
such as the arm. The Kalman filter is used to predict the human gesture. The human joints danger index is
used to evaluate the risk level of the human on the basis of human body joints and robot’s motion
information. Finally, a motion control strategy is adopted in view of obstacle categories and the human joint
danger index. Results show that the proposed method can effectively improve robot’s security in real time.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Stereo Vision Human Motion Detection and Tracking in Uncontrolled EnvironmentTELKOMNIKA JOURNAL
Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
Traditional motion capture (mocap) has been well-studied in visual science for the last decades. However the field is mostly about capturing precise animation to be used in specific applications after intensive post processing such as studying biomechanics or rigging models in movies. These data sets are normally captured in complex laboratory environments with sophisticated equipment thus making motion capture a
field that is mostly exclusive to professional animators. In addition, obtrusive sensors must be attached to actors and calibrated within the capturing system, resulting in limited and unnatural motion. In recent year the rise of computer vision and interactive entertainment opened the gate for a different type of motion capture which focuses on producing optical markerless or mechanical sensorless motion capture. Furthermore a wide array of low-cost device are released that are easy to use for less mission critical applications. This paper describes a new technique of using multiple infrared devices to process data from multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap using commodity
devices such as Kinect. The method involves analyzing each individual sensor data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasizes on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability.
MULTIPLE OBJECTS TRACKING IN SURVEILLANCE VIDEO USING COLOR AND HU MOMENTSsipij
Multiple objects tracking finds its applications in many high level vision analysis like object behaviour
interpretation and gait recognition. In this paper, a feature based method to track the multiple moving
objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu
moments features from the motion segmented object blob and establishing the association of objects in the
successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor
classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the
robustness of the proposed method. The proposed method is assessed quantitatively using the precision and
recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit
the efficacy of the proposed method.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Moving object detection using background subtraction algorithm using simulinkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stem calyx recognition of an apple using shape descriptorssipij
This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main
drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects,
this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to
recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises
of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and
small defects are detected using multi-threshold segmentation. The shape features are extracted from
detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are
recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated
using experiments conducted on apple image dataset and results exhibit considerable improvement in
recognition of stem-calyx region compared to other techniques.
TARGET LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON RECEIVED SIGNAL STRE...sipij
We consider the problem of localizing a target taking the help of a set of anchor beacon nodes. A small
number of beacon nodes are deployed at known locations in the area. The target can detect a beacon
provided it happens to lie within the beacon’s transmission range. Thus, the target obtains a measurement
vector containing the readings of the beacons: ‘1’ corresponding to a beacon if it is able to detect the
target, and ‘0’ if the beacon is not able to detect the target. The goal is twofold: to determine the location
of the target based on the binary measurement vector at the target; and to study the behaviour of the
localization uncertainty as a function of the beacon transmission range (sensing radius) and the number of
beacons deployed. Beacon transmission range means signal strength of the beacon to transmit and receive
the signals which is called as Received Signal Strength (RSS). To localize the target, we propose a gridmapping
based approach, where the readings corresponding to locations on a grid overlaid on the region
of interest are used to localize the target. To study the behaviour of the localization uncertainty as a
function of the sensing radius and number of beacons, extensive simulations and numerical experiments
are carried out. The results provide insights into the importance of optimally setting the sensing radius and
the improvement obtainable with increasing number of beacons.
The paper addresses the automation of the task of an epigraphist in reading and deciphering inscriptions.
The automation steps include Pre-processing, Segmentation, Feature Extraction and Recognition. Preprocessing
involves, enhancement of degraded ancient document images which is achieved through Spatial
filtering methods, followed by binarization of the enhanced image. Segmentation is carried out using Drop
Fall and Water Reservoir approaches, to obtain sampled characters. Next Gabor and Zonal features are
extracted for the sampled characters, and stored as feature vectors for training. Artificial Neural Network
(ANN) is trained with these feature vectors and later used for classification of new test characters. Finally
the classified characters are mapped to characters of modern form. The system showed good results when
tested on the nearly 150 samples of ancient Kannada epigraphs from Ashoka and Hoysala periods. An
average Recognition accuracy of 80.2% for Ashoka period and 75.6% for Hoysala period is achieved.
SENSITIVITY OF A VIDEO SURVEILLANCE SYSTEM BASED ON MOTION DETECTIONsipij
The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements.Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points.Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart
camera in industrial systems.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
Analysis of Human Behavior Based On Centroid and Treading TrackIJMER
Human body motion analysis is an important technology which modem bio-mechanics
combines with computer vision and has been widely used in intelligent control, human computer
interaction, motion analysis, and virtual reality and other fields. In which the moving human body
detection is the most important part of the human body motion analysis, the purpose is to detect the
moving human body with its behavior from the background image in video sequences, and for the follow-up treatment such as the target classification, the human body tracking and behavior understanding, its
effective detection plays a very important role
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
Robust Motion Detection and Tracking of Moving Objects using HOG Feature and ...CSCJournals
Detection and tracking of moving objects has gained significant importance due to intense technological progress in the field of computer science dealing with video surveillance systems. Human motion is generally nonlinear and non-Gaussian and thus many algorithms are not suitable for tracking. One of the applications to maintain universal security is crowd control. The main problem of video surveillance is continuous monitoring with regard to crime prevention. For security monitoring of live surveillance systems, target identification and tracking strategies can automatically send warnings to monitoring officers. In this paper, we propose a robust tracking of a specified person using the individuals' feature. The proposed method to determine automatic detection and tracking combines Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The Histogram oriented Gradient features are applied to single detection window for the identification of human area, after we use particle filters for robust specific people tracking using color and skin color based on the characteristics of a target individual. We have been improving the implementation, evaluation system of our proposed methods. In our systems, for experiments, we choose structured crowded scenes. From our experimental results, we have achieved high accuracy detection rates and robust motion tracking for specific targets.
A Robot Collision Avoidance Method Using Kinect and Global VisionTELKOMNIKA JOURNAL
This paper introduces a robot collision avoidance method using Kinect and global vision to
improve the industrial robot’s security. Global vision is installed above the robot, and a combination of the
background-difference method and the Otsu algorithm are used. Human skeleton detection is then
introduced to detect the location information of the human body. The collided objects are classified into
nonhuman and human obstacle which is further categorized into the human head and non-head areas
such as the arm. The Kalman filter is used to predict the human gesture. The human joints danger index is
used to evaluate the risk level of the human on the basis of human body joints and robot’s motion
information. Finally, a motion control strategy is adopted in view of obstacle categories and the human joint
danger index. Results show that the proposed method can effectively improve robot’s security in real time.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Stereo Vision Human Motion Detection and Tracking in Uncontrolled EnvironmentTELKOMNIKA JOURNAL
Stereo vision in detecting human motion is an emerging research for automation, robotics, and sports science field due to the advancement of imaging sensors and information technology. The difficulty of human motion detection and tracking is relatively complex when it is applied to uncontrolled environment. In this paper, a hybrid filter approach is proposed to detect human motion in the stereo vision. The hybrid filter approach integrates Gaussian filter and median filter to reduce the coverage of shadow and sudden change of illumination. In addition, sequential thinning and thickening morphological method is used to construct the skeleton model. The proposed hybrid approach is compared with the normalized filter. As a result, the proposed approach produces better skeleton model with less influential effect on shadow and illumination. The output results of the proposed approach can show up to 86% of average accuracy matched with skeleton model. In addition, obtains approximately 94% of sensitivity measurement in the stereo vision. The proposed approach using hybrid filter and sequential morphology could improve the performance of the detection in the uncontrolled environment.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
Traditional motion capture (mocap) has been well-studied in visual science for the last decades. However the field is mostly about capturing precise animation to be used in specific applications after intensive post processing such as studying biomechanics or rigging models in movies. These data sets are normally captured in complex laboratory environments with sophisticated equipment thus making motion capture a
field that is mostly exclusive to professional animators. In addition, obtrusive sensors must be attached to actors and calibrated within the capturing system, resulting in limited and unnatural motion. In recent year the rise of computer vision and interactive entertainment opened the gate for a different type of motion capture which focuses on producing optical markerless or mechanical sensorless motion capture. Furthermore a wide array of low-cost device are released that are easy to use for less mission critical applications. This paper describes a new technique of using multiple infrared devices to process data from multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap using commodity
devices such as Kinect. The method involves analyzing each individual sensor data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasizes on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability.
MULTIPLE OBJECTS TRACKING IN SURVEILLANCE VIDEO USING COLOR AND HU MOMENTSsipij
Multiple objects tracking finds its applications in many high level vision analysis like object behaviour
interpretation and gait recognition. In this paper, a feature based method to track the multiple moving
objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu
moments features from the motion segmented object blob and establishing the association of objects in the
successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor
classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the
robustness of the proposed method. The proposed method is assessed quantitatively using the precision and
recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit
the efficacy of the proposed method.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
Moving object detection using background subtraction algorithm using simulinkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stem calyx recognition of an apple using shape descriptorssipij
This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main
drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects,
this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to
recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises
of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and
small defects are detected using multi-threshold segmentation. The shape features are extracted from
detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are
recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated
using experiments conducted on apple image dataset and results exhibit considerable improvement in
recognition of stem-calyx region compared to other techniques.
TARGET LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON RECEIVED SIGNAL STRE...sipij
We consider the problem of localizing a target taking the help of a set of anchor beacon nodes. A small
number of beacon nodes are deployed at known locations in the area. The target can detect a beacon
provided it happens to lie within the beacon’s transmission range. Thus, the target obtains a measurement
vector containing the readings of the beacons: ‘1’ corresponding to a beacon if it is able to detect the
target, and ‘0’ if the beacon is not able to detect the target. The goal is twofold: to determine the location
of the target based on the binary measurement vector at the target; and to study the behaviour of the
localization uncertainty as a function of the beacon transmission range (sensing radius) and the number of
beacons deployed. Beacon transmission range means signal strength of the beacon to transmit and receive
the signals which is called as Received Signal Strength (RSS). To localize the target, we propose a gridmapping
based approach, where the readings corresponding to locations on a grid overlaid on the region
of interest are used to localize the target. To study the behaviour of the localization uncertainty as a
function of the sensing radius and number of beacons, extensive simulations and numerical experiments
are carried out. The results provide insights into the importance of optimally setting the sensing radius and
the improvement obtainable with increasing number of beacons.
The paper addresses the automation of the task of an epigraphist in reading and deciphering inscriptions.
The automation steps include Pre-processing, Segmentation, Feature Extraction and Recognition. Preprocessing
involves, enhancement of degraded ancient document images which is achieved through Spatial
filtering methods, followed by binarization of the enhanced image. Segmentation is carried out using Drop
Fall and Water Reservoir approaches, to obtain sampled characters. Next Gabor and Zonal features are
extracted for the sampled characters, and stored as feature vectors for training. Artificial Neural Network
(ANN) is trained with these feature vectors and later used for classification of new test characters. Finally
the classified characters are mapped to characters of modern form. The system showed good results when
tested on the nearly 150 samples of ancient Kannada epigraphs from Ashoka and Hoysala periods. An
average Recognition accuracy of 80.2% for Ashoka period and 75.6% for Hoysala period is achieved.
Automatic meal inspection system using lbp hf feature for central kitchensipij
This paper proposes an intelligent and automatic meal inspection system which can be applied to the meal
inspection for the application of central kitchen automation. The diet specifically designed for the patients are required with providing personalized diet such as low sodium intake or some necessary food. Hence,
the proposed system can benefit the inspection process that is often performed manually. In the proposed
system, firstly, the meal box can be detected and located automatically with the vision-based method and
then all the food ingredients can be identified by using the color and LBP-HF texture features. Secondly,
the quantity for each of food ingredient is estimated by using the image depth information. The experimental results show that the meal inspection accuracy can approach 80%, meal inspection efficiency can reach1200ms, and the food quantity accuracy is about 90%. The proposed system is expected to increase the capacity of meal supply over 50% and be helpful to the dietician in the hospital for saving the time in the diet inspection process.
A Hybrid Architecture for Tracking People in Real-Time Using a Video Surveill...sipij
This paper describes a novel method for tracking customers using images taken from video-surveillance
cameras. This system analyzes the number of customers and their motions through the aisles of big-box
stores (supermarkets) in real-time. The originality of our approach is based on the study of the blobs
properties for managing the splitting/merging issues using a mathematical morphology operator. In the
order hand, in order to manage a high number of customers in real-time, we combine the advantage of two
tracking algorithms.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
A R EVIEW P APER : N OISE M ODELS IN D IGITAL I MAGE P ROCESSINGsipij
Noise is always presents in digital images during
image acquisition, coding, transmission, and proces
sing
steps. Noise is very difficult to remove it from t
he digital images without the prior knowledge of no
ise
model. That is why, review of noise models are esse
ntial in the study of image denoising techniques. I
n this
paper, we express a brief overview of various noise
models. These noise models can be selected by anal
ysis
of their origin. In this way, we present a complete
and quantitative analysis of noise models availabl
e in
digital images.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
A BINARY TO RESIDUE CONVERSION USING NEW PROPOSED NON-COPRIME MODULI SETsipij
Residue Number System is generally supposed to use co-prime moduli set. Non-coprime moduli sets are a
field in RNS which is little studied. That's why this work was devoted to them. The resources that discuss
non-coprime in RNS are very limited. For the previous reasons, this paper analyses the RNS conversion
using suggested non-coprime moduli set.
This paper suggests a new non-coprime moduli set and investigates its performance. The suggested new
moduli set has the general representation as {2n
–2, 2n
, 2n+2}, where n ∈ {2,3,…..,∞}. The calculations
among the moduli are done with this n value. These moduli are 2 spaces apart on the numbers line from
each other. This range helps in the algorithm’s calculations as to be shown.
The proposed non-coprime moduli set is investigated. Conversion algorithm from Binary to Residue is
developed. Correctness of the algorithm was obtained through simulation program. Conversion algorithm
is implemented.
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
A FLUXGATE SENSOR APPLICATION: COIN IDENTIFICATIONsipij
Today, coins are used to operate many electric devices that are open to the public service. Washing machines, play stations, computers, auto brooms, foam machines, beverage machines, telephone chargers, hair dryers and water heaters are some examples of these devices These devices include coin recognition systems. In these systems, there are coils at two different radius, which become electromagnets when the
current is passed through them. The AC current supplied to the coils creates a variable magnetic field, which induces the eddy current on the coil during the passing of money. The magnetic field generated by the Eddy current reduces the current passing through the coil. The amount of change of current in the coil gives information about the coin; the type of metal (element) and the amount of metal (element). In this
study, a new coin identification system (magnetic measurement system) is designed. In this system, the
magnetic anomaly generated by the coin as a result of applying direct current to the coils is tried to be
detected by fluxgate sensor. In this study, sensor voltages are acquired in computer environment by using
developed electronic unit and LabVIEW based software. In the paper, experimental results have been
discussed in detail.
Efficient pu mode decision and motion estimation for h.264 avc to hevc transc...sipij
H.264/AVC has been widely applied to various applications. However, a new video compression standard,
High Efficient Video Coding (HEVC), had been finalized in 2013. In this work, a fast transcoder from
H.264/AVC to HEVC is proposed. The proposed algorithm includes the fast prediction unit (PU) decision
and the fast motion estimation. With the strong relation between H.264/AVC and HEVC, the modes,
residuals, and variance of motion vectors (MVs) extracted from H.264/AVC can be reused to predict the
current encoding PU of HEVC. Furthermore, the MVs from H.264/AVC are used to decide the search
range of PU during motion estimation. Simulation results show that the proposed algorithm can save up to
53% of the encoding time and maintains the rate-distortion (R-D) performance for HEVC.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
AN MINIMUM RECONFIGURATION PROBABILITY ROUTING ALGORITHM FOR RWA IN ALL-OPTIC...sipij
In this paper, we present a detailed study of Minimum Reconfiguration Probability Routing (MRPR) algorithm, and its performance evaluation in comparison with Adaptive unconstrained routing (AUR) and Least Loaded routing (LLR) algorithms. We have minimized the effects of failures on link and router failure in the network under changing load conditions, we assess the probability of service and number of light path failures due to link or route failure on Wavelength Interchange(WI) network. The computation complexity is reduced by using Kalman Filter(KF) techniques. The minimum reconfiguration probability
routing (MRPR) algorithm selects most reliable routes and assign wavelengths to connections in a manner that utilizes the light path(LP) established efficiently considering all possible requests.
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
DETERMINATION OF BURIED MAGNETIC MATERIAL’S GEOMETRIC DIMENSIONSsipij
It is important to find buried magnetic material’s geometric features that are parallel to the soil surface in
order to determine anti-tank and anti-personnel mine compatible to standards. So that it is possible to
decrease the number of false alarms by separating the samples that have got non-standard geometries. For
this purpose, in this study the anomalies occurred at horizontal component of the earth’s magnetic field by
buried samples are determined with magnetic sensor. In the study, KMZ51 AMR is used as the magnetic
sensor. The position-controlled movement of the sensor along x-y axis is provided with 2D scanning system.
Trigger values of sensor output are evaluated with respect to the scanning field. The experiments are
redone for the samples at different geometries and variables are defined for geometric analysis. The
experimental conclusions obtained from this paper will be discussed in detail.
CHARACTERIZING HUMAN BEHAVIOURS USING STATISTICAL MOTION DESCRIPTORsipij
Identifying human behaviors is a challenging research problem due to the complexity and variation of
appearances and postures, the variation of camera settings, and view angles. In this paper, we try to
address the problem of human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of temporal segments. Then for each
segment, we compute dense optical flow, which provides instantaneous velocity information for all the
pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192- dimensions. We then train a non-linear multi-class SVM that classify dif erent human behaviors with the
accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle.
This paper contain the study about vibration analysis for gearbox casing using finite element analysis
(FEA).The aim of this paper is to apply ANSYS software to determine the natural frequency of gearbox casing. The
objective of the project is to analyze differential gearbox casing of tata indigo cs vehicle for modal and stress
analysis. The theoretical modal analysis needs to be validated with experimental results from Fourier frequency
transformer (FFT) analysis. The main motivation behind the work is to go for a complete FEA of casing rather than
empirical formulae and iterative procedures.
Java Implementation based Heterogeneous Video Sequence Automated Surveillance...CSCJournals
Automated video based surveillance monitoring is an essential and computationally challenging task to resolve issues in the secure access localities. This paper deals with some of the issues which are encountered in the integration surveillance monitoring in the real-life circumstances. We have employed video frames which are extorted from heterogeneous video formats. Each video frame is chosen to identify the anomalous events which are occurred in the sequence of time-driven process. Background subtraction is essentially required based on the optimal threshold and reference frame. Rest of the frames are ablated from reference image, hence all the foreground images paradigms are obtained. The co-ordinate existing in the deducted images is found by scanning the images horizontally until the occurrence of first black pixel. Obtained coordinate is twinned with existing co-ordinates in the primary images. The twinned co-ordinate in the primary image is considered as an active-region-of-interest. At the end, the starred images are converted to temporal video that scrutinizes the moving silhouettes of human behaviors in a static background. The proposed model is implemented in Java. Results and performance analysis are carried out in the real-life environments.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Detection and Tracking of Moving Object: A SurveyIJERA Editor
Object tracking is the process of locating moving object or multiple objects in sequence of frames. Object
tracking is basically a challenging problem. Difficulties in tracking of an object may arise due to abrupt changes
in environment, motion of object, noise etc. To overcome such problems different tracking algorithms have been
proposed. This paper presents various techniques related to object detection and tracking..The goal of this paper
is to present a survey of these techniques.
A New Algorithm for Tracking Objects in Videos of Cluttered ScenesZac Darcy
The work presented in this paper describes a novel algorithm for automatic video object tracking based on
a process of subtraction of successive frames, where the prediction of the direction of movement of the
object being tracked is carried out by analyzing the changing areas generated as result of the object’s
motion, specifically in regions of interest defined inside the object being tracked in both the current and the
next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of
the object being tracked in the next frame using a function which measures the grade of dissimilarity
between the region of interest defined inside the object being tracked in the current frame and a moving
region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on
the process of subtraction of successive frames. Finally, the location of the moving region of interest in the
next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of
the object being tracked in the next frame. On the other hand, it is also designed a testing platform which is
used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These
virtual scenarios are exposed to heavily cluttered conditions where areas which surround the object being
tracked present a high variability. The results obtained with the proposed algorithm show that the tracking
process was successfully carried out in a set of virtual scenarios under different challenging conditions.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
Abnormal activity detection in surveillance video scenesTELKOMNIKA JOURNAL
Automated detection of abnormal activity assumes a significant task in surveillance applications. This paper presents an intelligent framework video surveillance to detect abnormal human activity in an academic environment that takes into account the security and emergency aspects by focusing on three abnormal activities (falling, boxing and waving). This framework designed to consist of the two essential processes: the first one is a tracking system that can follow targets with identify sets of features to understand human activity and measure descriptive information of each target. The second one is a decision system that can realize if the activity of the target track is "normal" or "abnormal” then energizing alarm when recognized abnormal activities.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...cscpconf
Motion detection and object segmentation are an important research area of image-video processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level techniques in video analysis, object extraction, classification, and recognition. The detection of moving object is significant in many tasks, such as video surveillance & moving object tracking. The design of a video surveillance system is directed on involuntary dentification of events of interest, especially on tracking and on classification of moving objects. An entropy based realtime adaptive non-parametric window thresholding algorithm for change detection is anticipated in this research. Based on the approximation of the value of scatter of sections of change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm accomplishes well for change detection with high efficiency.
A Framework for Human Action Detection via Extraction of Multimodal FeaturesCSCJournals
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
Similar to An Innovative Moving Object Detection and Tracking System by Using Modified Region Growing Algorithm (20)
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
An Innovative Moving Object Detection and Tracking System by Using Modified Region Growing Algorithm
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
DOI : 10.5121/sipij.2016.7203 39
AN INNOVATIVE MOVING OBJECT
DETECTION AND TRACKING SYSTEM BY
USING MODIFIED REGION GROWING
ALGORITHM
G.Sharmila Sujatha and Prof. V.Valli Kumari
Department of Computer Science and Systems Engineering, Andhra University,
Visakhapatnam, AP, India
ABSTRACT
The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the
limitations which are existing nowadays. Although high performance ratio for video object detection and
tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to
propose a novel video object detection and tracking technique so as to minimize the computational
complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature
extraction, background subtraction and hole filling. Originally the video clip in the database is split into
frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this
stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified
region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract
the multi features from the segmented image and the background image, the feature value thus obtained
are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The
foreground image is then subjected to morphological operations of erosion and dilation so as to fill the
holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus
the moving object is tracked in this stage. This method will be employed in MATLAB platform and the
outcomes will be studied and compared with the existing techniques so as to reveal the performance of the
novel video object detection and tracking technique.
KEYWORDS
Adaptive Median filter (AMF), Modified Region growing (MRG), Morphological Operation, Object
Detection, Tracking.
1. INTRODUCTION
Object detection is done to find out the presence of objects in video frame and to detect those
objects [12]. Then the object which is detected is categorized into various classes such as
humans, vehicles and other moving objects [13]. Object tracking involves the process of
segmenting a region of interest from video frames and tracking its motion and position [14].
Nowadays moving object tracking from the video sequences plays a vital role just because of its
enormous practical applications like visual surveillance, perceptual user interface, content-based
image storage and retrieval, athletic performance analysis etc [16]. But object tracking in
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
40
dynamic sequences like waving trees, fountains and oceans etc. still remains a tedious task [14].
Moving object detection includes video acquisition, object detection and tracking. Video
surveillance is nothing but the process of examining video sequences [17], and also an effective
area in computer vision, which yields large amount of storage and display. Video surveillance is
broadly classified into three categories they are manual, semi-autonomous and fully-autonomous
[13].
In security surveillance identifying a moving object, whether it is a human or non-human plays a
vital role in most areas [19]. Human detection is a crucial area in research nowadays because of
the rise in theft, terrorist and other illegal actions [20]. A structure which allows real time
recognition of humans from a fixed camera makes the automatic video surveillance systems more
dependable an efficient [10]. The three major steps in video analysis are detection, tracking and
analyzing. (i.e. detecting the objects which are moving, tracking those objects frame by frame
and analyzing the tracks of that object to identify their behavior). So the application of object
tracking is suitable in tasks such as:
• Motion-based recognition, which means identification of humans on the basis of gait,
automatic object detection etc;
• Automatic surveillance, which means observing a scene to identify dubious activities or
improbable actions;
• Video indexing, which means automatic annotation and retrieving the videos in
multimedia databases;
• Human-computer interaction, which means gesture recognition, eye gaze tracking for
inputting the data into computers;
• Traffic monitoring, which means directing traffic flow by gathering real-time traffic
statistics;
• Vehicle navigation, which means path planning based on videos and capability of
avoiding obstacles.
For simple Object Detection and Tracking several techniques or algorithms are used. Although
these techniques are implemented for different objectives like noise reduction or working on
specific images (e.g. Gray scale images) or to diminish complications; etc; limitations are there
while applying these algorithms or techniques. The techniques used nowadays for moving object
detection are the frame subtraction method, the background subtraction method and the optical
flow method [1, 2]. Frame subtraction is nothing but it takes the difference of two consecutive
frames in order to identify the moving objects [13]. The computation for the frame subtraction
method is uncomplicated and simple to develop. Moving Object Detection with Moving Camera
is very difficult job[27]. Most of the algorithms focus independently to solve the object detection
problem or the tracking problem [9]. Even though there are different algorithms available to solve
all the problems, but the desirable option is to combine these algorithms so that an optimal
solution could be obtained for the problems [13, 7].
The organization of this paper is arranged as follows. In section 2, a concise talk about the
relevant research works are specified. In section 3, our novel object detection and tracking
technique is described concisely. Section 4 includes the implementation results and performance
analysis. Section 5 terminates the paper.
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
41
2. RECENT RELATED RESEARCHES: A REVIEW
Shuai Zhang et al. [21] projected an object detection technique by means of mean shift (MS)
segmentation algorithm. The objects were again separated by using depth information which was
derived from stereo vision. The objects which were detected from the above technique were then
tracked by means of a novel object tacking algorithm with the help of a new Bayesian Kalman
Filter (BKF) with the Simplified Gaussian Mixture (BKF-SGM). This BKF-SGM uses a GM
representation of both static and noise densities. In order to avoid exponential complexity growth
of conventional KFs with GM an innovative direct density simplifying algorithm is used. More
robust tracking performance is achieved, when this BKF-SGM is coupled with an improved MS
tracker, and the algorithm so obtained is called BKF-GSM-IMS algorithm.
Karthick Muthuswamy et al. [22] proposed a technique to generate spatial and motion saliency
maps on the basis of comparison of local features and dominant features which are available in
the frame. In this method salient region was marked if there was a huge variation between local
and dominant features. Hue and Saturation features were used in spatial saliency and Optical flow
vector features were used in motion saliency. Then the function was estimated by segmentation
datasets. A simple algorithm was developed so as to create spatio-temporal saliency map which
carry out a lot of state-of-the-art techniques.
Meenakshi Gupta et al. [23] proposed a technique called background subtraction method which
has a combination of both depth segmentation detector and template matching technique so as to
automatically initialize the human tracking. In order to the track the human silhouette in an active
surrounding, when the robot was in motion, an innovative idea of head and hand creation on the
basis of depth of interest was presented in that paper. A series of detectors (e.g. height, size and
shape) was used in order to differentiate human from other moving objects so as to make this
algorithm or technique powerful. As this silhouette-matching-based-method requires somewhat
more calculating time the matching-based-method was used only when it was required. The
location of humans in the frame was predicted by an unscented Kalman filter in order to sustain
the stability of robotic motion.
Hailing Zhou et al. [24] presented proficient road detection and tracking framework in UAV
videos. This road detection and tracking was done by two approaches they are graph-cut-based-
detection and homography-based road tracking. During the initial stage a graph-cut-based-
detection was used so as to extract a specific region in the road, and for tracking a fast
homography-based road tracking was used in order to track the road areas automatically. Graph-
cut-based-detection was performed when there was a need to detect the road and generally most
of the work was done by the fast homography-based road tracking.
Ahlem Walha et al. [25] proposed a system called video stabilization and moving object
detection on the basis of camera movement evaluation. The global motion was calculated
approximately with the help of local feature extraction and matching. For stabilization task they
reveal the Scale Invariant Feature Transform (SIFT) key points. When the approximate
calculation was done the global camera motion parameters uses affine transformation, where the
moving objects are detected using Kalman filtering. A median filter was used to maintain the
desired motion for motion smoothing. At the final point in order to get the stabilized video
sequence, motion compensation was performed. The efficiency of the proposed system was
revealed by numerous aerial video examples. The above system uses the software Virtual Dub
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
42
with Deshaker-plugin for testing purposes. For objective estimation they employ Interframe
Transformation Fidelity and for the moving object detection task they utilize the Detection Ratio.
Bodhisattwa Chakraborty et al. [26] projected a technique called trajectory based ball-detection-
and-tracking-method so as to detect and track the ball in a group of videos of basketball long shot
sequences. The methodology used in the above paper is a two-fold detection and tracking
framework, in the initial phase the detection of ball was done with the help of feature based
method. The second phase involves the verification of the first stage, that is whether the detected
object obtained in the first stage was ball or not, this was performed with the help of 2D
trajectory information from the possible ball candidates in the frame. From the collection of
candidate trajectories a proper ball trajectory was obtained and the ball locations were predicted
from the obtained trajectories. Because of the occlusion of the ball and assimilation of the ball
image with other objects from the background image ball positions were missed. These missed
ball positions were predicted with the help of trajectory interpolation technique.
3. THE PROPOSED MOVING OBJECT DETECTION AND TRACKING
SYSTEM USING MODIFIED REGION GROWING METHOD
Our proposed technique makes use of modified region growing algorithm for moving object
detection. The object thus detected has been tracked with the help of multi features threshold
value. We use video as an input which has been obtained from the database. Originally, the input
video which has been obtained from the data base has split up into many frames.
Let ( )dV be the video database and }nv…v2,{v1 ji,ji,ji, be the total number of frames
in the video.
The frames thus obtained undergo preprocessing so as to eliminate the noise present in the
frames. We have used an adaptive median filter in this preprocessing phase in order to get rid of
noise. Segmentation has been done from the pre-processed image obtained from the above stage
with the help of modified region growing algorithm. Subsequently, the background in the video
frame has been subtracted by means of frame differencing method. From the segmented frame
and its related background frame the multi-features like color, texture, motion, wavelet, edge has
been taken out. The distance between background features and segmented features were
computed by means of block matching algorithm and the result so obtained is compared with the
threshold value. If it suits the threshold value it is considered as foreground object, morphological
operations like opening and closing operations has been performed in the foreground object so as
to eliminate the holes and unwanted breaks present in it.
Our proposed system deals with five phases they are,
i) Pre-processing
Adaptive median filter
ii) Object detection
Modified region growing algorithm
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
43
iii) Feature extraction
Color
Texture
Motion
Wavelet
Edge
Mean
Standard deviation
Skewness
iv) Hole filling
Morphological operation.
Figure1: Architecture of our proposed technique
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44
3.1 Preprocessing by utilizing Adaptive Median Filter (AMF)
Pre-processing was done in the input frames by means of Adaptive Median Filter (AMF) in order
to eliminate the noise present in the frames. Initially it detects the probable noisy pixels,
subsequently it interchange them by means of its variants while the good pixels in the image were
kept unaffected. The AMF filter performs well for revealing the noise still at a high noise level.
90% of the salt-and-pepper noise was eliminated by means of this filter. Noise removal from the
input image should be made mandatory in order to attain better accuracy. Adaptive Median Filter
performs based on the local statistics characters where the impulse was detected by manipulating
the variation between the standard deviation of the pixels inside the filter window and the related
current pixel.
Consider the video ( )dV contains the video frame of n numbers. Let jiv , be one of the grey
level frame in the image v at location ( )ji, taken from the video. Let maxmin , vv are the
lower and upper bounds of the image v .
( ) ajivvv ji ∈∀≤≤ ,max,min , { } { }nma ,...2,1...2,1 ×≡ ,
The pixel location ( )ji, for the grey level image v is given by the probability
−−
=
qpv
qyprobabilitwithv
pyprobabilitwithv
p
ji
x
x
ji
1,
,
,
,
max
min
, (1)
The noise level, xx qpln +=)( (2)
maxmin , vv , Local mean value )(µl of the moving window and Local standard
deviation )(σl are computed.
Standard deviation and a user defined multiplier upper and lower bounds are computed
subsequently with the help of local mean.
3.2 Object detection:
3.2.1 Segmentation using modified region growing algorithm:
For image segmentation region growing technique is a renowned method, where the
segmentation is done based on seed point selection. Initially, in order to check whether the
neighboring pixels in the image can be included with the region or not, the neighboring pixels
are compared with the initial seed points. In segmentation process, the seed point selection plays
a crucial role. The normal Region Growing Technique selects the seed points based on the
intensity threshold, which fallout to over-segmentation or holes due to the imperfections in the
image such as noise or deviation in intensity. And also by using the normal region growing
technique the shadings of real images not get differentiated. In order to overcome these
limitations, the intensity and orientation threshold features are included while selecting the seed
points in the pre-processed images. The step by step procedure of MRG technique is illustrated
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
45
below:
Step 1: Calculate the gradient of the frames for x axis and y axis, let it be )ˆ( rdxg and )ˆ( rdyg .
Step 2: Integrate the gradient values based on the Equation shown below, so as to find the
gradient vector GV
( )22
ˆˆ1
1
rdrd ygxg
GV
++
= (3)
Step 3: Attain the values of orientation by modifying the gradient vector values from radians to
degrees.
Step 4: Segment the image into grids igrd .
Step 5: Ordain the intensity threshold as ( ))(thldI and orientation threshold as( ))(thldO .
Step 6: Continue the subsequent procedures in step 7 regarding each grid igrd , until the number
of grids reach the total number of grids for an image.
Step 7(a): Find out the histogram )(hsgm of each pixel in igrd .
step 7(b): Choose the most frequent histogram of the igrd th
grid and it denoted as )(hsgmFreq .
Step 7(c): Select any pixel, as per )(hsgmFreq and assign that pixel as the seed point which has the
intensity( )pIy and Orientation( )pOn .
Step 7(d): The intensity and orientation of the neighboring pixel is considered as ( )nIy and
( )nOn .
Step 7(e): Determine the intensity and orientation difference of the pixels p and n by using the
equations given below.
np IyIydif −=int (4)
nporient OnOndif −= (5)
Step 7(f): If )(int thldIdif ≤ && thldorient Odif ≤ , then make the region to grow by adding the related
pixel to the region or else, go to step 7(h).
Step 7(g): Scrutinize whether all the pixels are added to the region. If yes, go to step 6, or else go
to step 7(h).
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46
Step 7(h): Re-examine the region and locate the new seed points and perform the procedure from
step 7(a).
Step 8: Terminate the entire procedure.
Thus the moving object is segmented from the video frames by means of modified region
growing algorithm.
3.3 Feature extraction :
The unique form of dimensional reduction is termed as feature extraction. If the input data of an
algorithm for processing is more it is so called disreputably redundant. Transformation of the
input data will be done so as to get the reduced representation set of features. Transforming the
input data into the set of features is named as feature extraction. From the segmented and the
background image the multi features such as color, texture, motion, wavelet, edge, mean standard
deviation, and skewness were extracted during the feature extraction stage of our technique.
3.3.1 Color feature
For multimedia context and video retrieval, color is one of the important visual features . In our
technique the color features are extracted by means of applying histogram on the video shots.
This histogram provides the compact summary of the distribution of data in an image or video.
Initially re-sampling process is carried out for the image after that anisotropic diffusion process is
carried out then for the anisotropic diffused image histogram is applied.
Histogram
Color histogram is nothing but a K-dimensional vector. Each component kh in this vector
signifies the relative number of pixels of color kC in the image, which means the fraction of the
pixels are nearly alike with the equivalent color. In order to construct the color histogram,
transformation and quantization should be done. In the former, the image colors are transformed
to the suitable color space and in the latter, quantization should be done based on a specific
codebook of size K.
,)(
N
n
iI ocr
h = gli <≤0 (6)
Where, gl denotes the total number of gray levels in the image, ocrn denotes the number of
occurrences of gray leveli . N denotes the total number of pixels in the image, and iI h)(
denotes the image histogram for pixel valuei .
9. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
47
3.3.1 Texture feature (LGXP)
In our technique, Texture feature extraction is done by means of local gabor xor patten. LGXP
operator is employed for the quantized phases of the central pixels and its neighbors, which is
calculated from the equation mentioned below
))(()(( ,,, ihch
g
h zXORqzqLGXP µµµ ϕϕ= (7)
Where ),...,2,1(, KKLGXPg
h ==µ signifies the pattern calculated between )(, ihµϕ and its
neighbor cz , )(, ch zµϕ denotes the phase, ))(( , ih zq µϕ denotes the quantized value of the phase
and )(, lhµϕ denotes the position of the central pixel in the Gabor phase map of scale w and
orientation k,µ denotes the neighborhood size.
At last the consequential binary labels are concatenated together so as to form a local pattern of
the central pixel.
∑−
−−
==
k
i
i
h
i
binaryh
k
h
k
hch LXGPLGXPLGXPLGXPzLGXP
1
,
11
,
1
,,, ,2]....,.........,[)( µµµµµ (8)
The outcome of the LGXP process is an equivalent value and the original value gets replaced by
this equivalent value, this procedure is done for all blocks
3.3.3 Motion feature extraction using block matching algorithm
In a video sequence, the process of detecting the motion vectors which forms the transition from
one frame to other is termed as motion estimation. Based on the motion of the object, we can be
able to detect and track the object in any video. In our proposed technique, for motion estimation
we have used the block matching algorithm.
3.4 Object tracking using dissimilarity calculation
During object tracking phase, the segmented image features and the background image features
are compared and the dissimilarities between them will be computed. The outcome thus obtained
is compared with the threshold value )(η
)
.The equation given below clearly illustrates the
dissimilarity calculation,
An object is said to be tracked, when the object distance satisfies the threshold condition in the
equation demonstrated above. The object thus tracked undergoes post processing. The holes and
>
≤
=
tydisimilaricorrectnot
tydisimilaricorrect
objecttracked
)(,
,)(,
η
η
)
)
10. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
48
the discontinuities elimination of the tracked object is done in the post processing stage by means
of morphological operation.
3.5 Post processing using morphological operation
Images are performed. During opening, the original image is eroded and subsequently it
undergoes dilation. During closing, the original The object tracked using dissimilarity calculation
contains holes and discontinuities which are removed by means of morphological operations like
erosion and dilation.
Erosion and dilation operations are nothing but the minimization and maximization value in the
window. After performing the erosion and dilation operations, the intensity of the image get
enhanced or it is brighter than the original image. Subsequently the image enlarges and shrinks
(thins) by using these operations. The enlarged image is used frequently to fill the spaces. By
means of the erosion and dilation operations the opening and closing of the image is dilated and
subsequently it undergoes erosion.
3.5.1 Operations of Opening and Closing
The outcome of the opening operation is that it makes the shape of the object smooth, cuts off the
thin, irregular and removes thin protrusions. In closing operation, it makes the shape of the object
smooth; but in contrast to opening operation it eliminates the discontinuities and bridges the long,
thin gap, gets rid of tiny holes and fills the cracks in the contour line.
3.5.1.1 Erosion operation
The erosion operation is one of the essential parts in morphological processing. In binary image
the erosion process makes the object to “shrink” or “thin”. The shrink operation is done by means
of a structure element
3.5.1.2 .Dilation Operation
The dilation operation is also a necessary part in morphological processing. In binary image the
dilation operation makes the object to “lengthen” or “thick”. The “lengthening” or "thickening"
process in dilation also done by means of a structure element likewise in erosion operation.
4. EXPERIMENTAL RESULTS AND DISCUSSION
Our novel technique of moving object detection and tracking system is employed in the
MATLAB platform (version 14 a) with machine configuration as mentioned below:
Processor: Intel core i3
OS: Windows 7
CPU speed: 2.40 GHz
RAM: 4GB
The performance of our proposed technique of moving object detection and tracking system
based on modified region growing technique and multi feature thresholding is assessed by means
11. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
49
of different videos. The fig 3 are the original images attained from shot segmentation process. Fig
4 are the preprocessed frames. Fig5 (a) shows the resultant images achieved during the object
detection process and at last fig 6 (a) represents the tracked images in the video The sample
images obtained from the video (1) and video (2) are shown in Fig.3 to 5.
Figure 3 are the original frames obtained by means of shot segmentation process in video
In order to eliminate the noise from the above frames it undergoes preprocessing by means of an
adaptive filter. Fig. 4 represents the frames obtained after noise removal in the preprocessing
stage.
Figure 4 shows the pre-processed frames in video
Inorder to detect the object from the frames obtained above it then undergoes segmentation
process by means of modified region growing algorithm. The segmented object obtained from the
above frames are shown in fig 5.
Fig 5
4.1 Performance Analysis
The performance of our proposed method of moving object detection and tracking
system is studied by employing the statistical measures mentioned below.
FBTP
FP
FARRateAlarmFalse
+
=)( (18)
FNTP
TP
RateDetection
+
= (19)
TNFP
TN
ySpecificit
+
= (20)
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50
TF
TNTP
Acuuracy
+
= (21)
FPTP
TP
predictionPosition
+
= (22)
TNFN
TN
predictionNegative
+
= (23)
TBFN
FN
RateNegativeFalse
+
= (24)
TNFP
FP
RatePositiveFalse
+
= (25)
))()()(( FNTNFPTNFNTPFPTP
FNFPTNTP
MCC
++++
×−×
= (26)
4.2 Comparative analysis:
Comparative analysis includes the comparison of the proposed modified region growing
algorithm with the existing region growing algorithm and fuzzy c means algorithm based on the
conditions of statistical measures like accuracy, sensitivity, detection rate, NPV, PPV, FNR, FPR,
MCC and FAR for the video the results obtained are shown in table.
Measures Proposed modified
region growing
Existing region
growing
Fuzzy C means
Accuracy 0.980149 0.9733639 0.9787655
Specificity 0.848101 0.726918 0.771522
Detection Rate 0.999475 0.978306 0.98289
NPV 0.978239 0.994422 0.995388
PPV 0.995752 0.402993 0.475434
FNR 0.000525 0.021694 0.01711
FPR 0.151899 0.273082 0.228478
MCC 0.908562 0.529102 0.59548
FAR 0.021761 0.005578 0.004612
Table 1: Exemplifies the performance measures of the proposed moving object detection technique and the
existing techniques of region growing algorithm and fuzzy c means for the video.
Discussion
Table 1 demonstrates the performance measures of our proposed moving object detection and
tracking system for the video (2) which shows the improved accuracy, specificity and detection
13. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
51
rate of the proposed technique as when compared with the existing RGA and FCM techniques.
This improved performance ratio indicates that our novel technique detects and tracks the object
more accurately than the existing RGA and FCM techniques. The performance comparison
graphs of accuracy, specificity and detection rate are exemplified in fig 6.
(i)
(ii)
(iii)
Figure 6: Exemplifies the performance graph in terms of (i) Accuracy, (ii) Specificity and detection rate of
moving object detection for the proposed technique and the existing techniques of modified region growing
algorithm and fuzzy c means techniques for the video.
Moreover our proposed technique computational time is compared with the previous paper. And
also with the existing techniques such as FCM and RGA and the comparison graph has been
plotted below.
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52
(i)
(ii)
Figure 7: shows the performance graph in terms of computational time
Discussion:
In the figure 7(i) our proposed technique is compared with the previous work. Similarly in figure
7(ii) our proposed technique is compared with existing algorithms such as FCM and Region
growing. From the graph it has been clearly shown that our proposed technique consumes very
low computational time than any other techniques. Hence it is proved that our proposed technique
detects and tracks the object efficiently in low computational timing.
5. CONCLUSION
In this paper, we focus on the advancement in moving object detection and tracking system. In
order to diminish limitations in the existing techniques, we have deliberately proposed moving
object detection method by means of modified region growing technique. In comparative
analysis, the performance of our technique is compared with the existing technique which shows
that our novel moving object detection and tracking system yields improved performance.
Moreover, the comparison results show that the accuracy and the computation time of our
proposed method are better than the existing techniques. Consequently, it is proved that our
proposed moving object detection and tracking system detects the object with more accuracy and
proficiency and tracks the moving object with high performance and less computation time as
when compared with the existing techniques.
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53
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[27] G.Sharmila Sujatha and Prof. V.Valli Kumari, "An Efficient Motion Based Video Object Detection
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AUTHORS
Prof. Valli Kumari , Deptartment of CS&SE,College of Engg,.Andhra University
She has total Teaching Experience 24 years.she also Honorary Director, Andhra
University Computer Centre. She got Gold Medal for best Research (PhD thesis in
Engineering:2004-2005), awarded at 75th Convocation (Diamond Jubilee) of
Andhra University, January 2008. Best Paper award,INDICON-2011(IEEE
International Conference held at BITS PILANI, Hyderabad campus, December,
2011) ,more than 60 papers published in conferences and journals,she had more than
five editorial memberships, she is member of more than 20 reviewing
committe/technical committes. she is author of Privacy Preserving Publishing: A compilation of state of
17. Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016
55
the art works for researchers, OO Programming in Java, 2001, for School for Distance Education, Andhra
University, Software Engineering, Printed and circulated, by CSE Association, , S.R.K R Engineering
College ,1994,She is member of IEEE,ACM ,IETE more than this. She Two international level chapters.
G. Sharmila Sujatha working Assistant Professor (c ) in Dept of CS&SE,Andhra
University since 2001,Research area is Image Processing.Ten workshop/seminors
attended,Three international Papers,Two international journals and Two national
level papers are published.