The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
Applying edge density based region growing with frame difference for detectin...eSAT 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
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
Object Detection and tracking in Video SequencesIDES Editor
This paper focuses on key steps in video analysis
i.e. Detection of moving objects of interest and tracking of
such objects from frame to frame. The object shape
representations commonly employed for tracking are first
reviewed and the criterion of feature Selection for tracking is
discussed. Various object detection and tracking approaches
are compared and analyzed.
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.
Applying edge density based region growing with frame difference for detectin...eSAT 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
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
Object Detection and tracking in Video SequencesIDES Editor
This paper focuses on key steps in video analysis
i.e. Detection of moving objects of interest and tracking of
such objects from frame to frame. The object shape
representations commonly employed for tracking are first
reviewed and the criterion of feature Selection for tracking is
discussed. Various object detection and tracking approaches
are compared and analyzed.
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.
An Innovative Moving Object Detection and Tracking System by Using Modified R...sipij
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.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
Comparison of Some Motion Detection Methods in cases of Single and Multiple M...CSCJournals
Motion detection tells us whether there is a change in position of an object with respect to its surroundings or vice versa. It is applied to various domestic and commercial applications starting from simple motion detectors to high speed video surveillance systems. In this paper, results obtained from some simple motion detection algorithms, which use methods like image subtraction and edge detection, have been compared. The software used for this purpose was MATLAB 7.6.0 (R2008a). It has been observed that while image subtraction is sufficient to detect motion in a video stream, combining it with edge detection in different sequences yields different results in different scenarios.
Occlusion and Abandoned Object Detection for Surveillance ApplicationsEditor IJCATR
Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Automatic 3D view Generation from a Single 2D Image for both Indoor and Outdo...ijcsa
Image based video generation paradigms have recently emerged as an interesting problem in the field of robotics. This paper focuses on the problem of automatic video generation of both indoor and outdoor scenes. Automatic 3D view generation of indoor scenes mainly consist of orthogonal planes and outdoor scenes consist of vanishing point. The algorithm infers frontier information directly from the images using a geometric context-based segmentation scheme that uses the natural scene structure. The presence of floor is a major cue for obtaining the termination point for the video generation of the indoor scenes and vanishing point plays an important role in case of outdoor scenes. In both the cases, we create the navigation by cropping the image to the desired size upto the termination point. Our approach is fully automatic, since it needs no human intervention and finds applications, mainly in assisting autonomous cars, virtual walk through ancient time images, in architectural sites and in forensics. Qualitative and quantitative experiments on nearly 250 images in different scenarios show that the proposed algorithms are more efficient and accurate.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
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.
A Detailed Analysis on Feature Extraction Techniques of Panoramic Image Stitc...IJEACS
Image stitching is a technique which is used for attaining a high resolution panoramic image. In this technique, distinct aesthetic images that are imaged from different view and angles are combined together to produce a panoramic image. In the field of computer graphics, photographic and computer vision, Image stitching techniques are considered as current research areas. For obtaining a stitched image it becomes mandatory that one should have the knowledge of geometric relations among multiple image co-ordinate system [1].First, image stitching will be done based on feature key point matches. Final image with seam will be blended with image blending technique. Hence in this paper we are going to address multiple distinct techniques like some invariant features as Scale Invariant Feature Transform and Speeded up Robust Transform and Corner techniques as Harris Corner Detection Technique that are useful in sorting out the issues related with stitching of images.
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.
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.
An Innovative Moving Object Detection and Tracking System by Using Modified R...sipij
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.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
Comparison of Some Motion Detection Methods in cases of Single and Multiple M...CSCJournals
Motion detection tells us whether there is a change in position of an object with respect to its surroundings or vice versa. It is applied to various domestic and commercial applications starting from simple motion detectors to high speed video surveillance systems. In this paper, results obtained from some simple motion detection algorithms, which use methods like image subtraction and edge detection, have been compared. The software used for this purpose was MATLAB 7.6.0 (R2008a). It has been observed that while image subtraction is sufficient to detect motion in a video stream, combining it with edge detection in different sequences yields different results in different scenarios.
Occlusion and Abandoned Object Detection for Surveillance ApplicationsEditor IJCATR
Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Automatic 3D view Generation from a Single 2D Image for both Indoor and Outdo...ijcsa
Image based video generation paradigms have recently emerged as an interesting problem in the field of robotics. This paper focuses on the problem of automatic video generation of both indoor and outdoor scenes. Automatic 3D view generation of indoor scenes mainly consist of orthogonal planes and outdoor scenes consist of vanishing point. The algorithm infers frontier information directly from the images using a geometric context-based segmentation scheme that uses the natural scene structure. The presence of floor is a major cue for obtaining the termination point for the video generation of the indoor scenes and vanishing point plays an important role in case of outdoor scenes. In both the cases, we create the navigation by cropping the image to the desired size upto the termination point. Our approach is fully automatic, since it needs no human intervention and finds applications, mainly in assisting autonomous cars, virtual walk through ancient time images, in architectural sites and in forensics. Qualitative and quantitative experiments on nearly 250 images in different scenarios show that the proposed algorithms are more efficient and accurate.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
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.
A Detailed Analysis on Feature Extraction Techniques of Panoramic Image Stitc...IJEACS
Image stitching is a technique which is used for attaining a high resolution panoramic image. In this technique, distinct aesthetic images that are imaged from different view and angles are combined together to produce a panoramic image. In the field of computer graphics, photographic and computer vision, Image stitching techniques are considered as current research areas. For obtaining a stitched image it becomes mandatory that one should have the knowledge of geometric relations among multiple image co-ordinate system [1].First, image stitching will be done based on feature key point matches. Final image with seam will be blended with image blending technique. Hence in this paper we are going to address multiple distinct techniques like some invariant features as Scale Invariant Feature Transform and Speeded up Robust Transform and Corner techniques as Harris Corner Detection Technique that are useful in sorting out the issues related with stitching of images.
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.
A NOVEL METHOD FOR PERSON TRACKING BASED K-NN : COMPARISON WITH SIFT AND MEAN...sipij
Object tracking can be defined as the process of detecting an object of interest from a video scene and
keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a
challenging problem and it’s an important task. Many researchers are getting attracted in the field of
computer vision, specifically the field of object tracking in video surveillance. The main purpose of this
paper is to give to the reader information of the present state of the art object tracking, together with
presenting steps involved in Background Subtraction and their techniques. In related literature we found
three main methods of object tracking: the first method is the optical flow; the second is related to the
background subtraction, which is divided into two types presented in this paper, then the temporal
differencing and the SIFT method and the last one is the mean shift method. We present a novel approach
to background subtraction that compare a current frame with the background model that we have set
before, so we can classified each pixel of the image as a foreground or a background element, then comes
the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is
divided into two different methods, the surface method and the K-NN method, both are explained in the
paper. Our proposed method is implemented and evaluated using CAVIAR database.
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.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
A Novel Background Subtraction Algorithm for Person Tracking Based on K-NN cscpconf
Object tracking can be defined as the process of detecting an object of interest from a video
scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers
are getting attracted in the field of computer vision, specifically the field of object tracking in
video surveillance. The main purpose of this paper is to give to the reader information of the
present state of the art object tracking, together with presenting steps involved in Background
Subtraction and their techniques. In related literature we found three main methods of object
tracking: the first method is the optical flow; the second is related to the background
subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current
frame with the background model that we have set before, so we can classified each pixel of the
image as a foreground or a background element, then comes the tracking step to present our
object of interest, which is a person, by his centroid. The tracking step is divided into two
different methods, the surface method and the K-NN method, both are explained in the paper.
Our proposed method is implemented and evaluated using CAVIAR database.
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
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
Integration of poses to enhance the shape of the object tracking from a singl...eSAT Journals
Abstract In computer vision, tracking human pose has received a growing attention in recent years. The existing methods used multi-view videos and camera calibrations to enhance the shape of the object in 3D view. In this paper, tracking and partial reconstruction of the shape of the object from a single view video is identified. The goal of the proposed integrated method is to detect the movement of a person more accurately in 2D view. The integrated method is a combination of Silhouette based pose estimation and Scene flow based pose estimation. The silhouette based pose estimation is used to enhance the shape of the object for 3D reconstruction and scene flow based pose estimation is used to capture the size as well as the stability of the object. By integrating these two poses, the accurate shape of the object has been calculated from a single view video. Keywords: Pose Estimation, optical Flow, Silhouette, Object Reconstruction, 3D Objects
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 Survey on Approaches for Object Trackingjournal ijrtem
ABSTRACT : Object detection and tracking has been a widely studied research problem in recent years. Currently system architectures are service oriented i.e. they offer number of services. All such common services are grouped together and are available as a domain called as service domain. One such service domain of our interest is LBS (location based service). The service of our interest is tracking. Tracking of moving objects is done in applications like surveillance systems, human computer interactions, object recognition, navigation systems etc. In real world, 3D, the object which we want to track is called as object of interest (OOI). Tracking has been a difficult task to apply in congested situations due to inaccurate segmentation of objects. Common problems of erroneous segmentation are long shadows, partial and full occlusion of objects with each other and with stationary items in the scene. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. In this paper we analyze different approaches for moving object tracking and detection. Keywords: Multiple moving object tracking, background modeling, morphology, target localization and representation, visual surveillance.
Threshold based filtering technique for efficient moving object detection and...eSAT Journals
Abstract Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is
used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has
been developed for video object detection, however the object detection accuracy and background object detection in the video
frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and
Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video
file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving
the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects
in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function,
particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking
accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to
enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT
framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with
the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics
such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object
detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve
the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe-
art works.
Keywords: Object segmentation, Object tracking, Object Detection, Enhanced Laplacian Thresholding, Median
Filter, Color Histogram-based Particle Filter
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COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
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and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
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1. The International Journal Of Engineering And Science (IJES)
|| Volume || 3 || Issue || 6 || Pages || 25-29 || 2014 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
www.theijes.com The IJES Page 25
Object Tracking Techniques for Video Tracking: A Survey
1,
Mansi Manocha, 2,
Parminder Kaur
1,2,
Department of Electronics And Communication, CEC Landran
------------------------------------------------ABSTRACT----------------------------------------------------------
Object detection and tracking are the tasks that are important and challenging in various computer vision
applications such as surveillance, vehicle navigation, and autonomous robot navigation. Video surveillance works in
a dynamic environment, especially for humans and vehicles. It is a technology helpful in fighting against
terrorism, crime, public safety and for efficient management of traffic. Detection of moving objects from a video is
important for object detection, target tracking, and understanding behaviour in video surveillance. Tracking of
stationary foreground regions is one of the most important requirements for surveillance systems based on the
tracking of abandoned or stolen objects or parked vehicles. To detect stationary foreground objects, the use of Object
tracking techniques is the most popular choice. The objective of this paper is to highlight the various techniques of
object tracking. This paper shows how one can simplify tracking by imposing constraints on the motion or
appearance of objects. Prior knowledge about the number and the size of objects, or the object appearance and
shape helps to simplify the problem. Numerous approaches for object tracking have been discussed.
KEYWORDS: tracking, frames, pixel, detection, tracking, spatial
---------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: 10 June 2014 Date of Publication: 30 June 2014
---------------------------------------------------------------------------------------------------------------------------------------
I. INRODUCTION
Object tracking holds an important place in the field of computer vision. Object detection involves
putting objects in frames of a video sequence. The location of moving objects or multiple objects over a period
of time using a camera is called tracking. Technically, tracking is the problem of estimating the trajectory or
path of an object in the image plane as it moves around a scene[1]. The high-powered computers, the
availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis
has generated a great deal of interest in object tracking algorithms. There are three key steps in video analysis:
[1] Detection of interesting moving objects.
[2] Tracking of objects from frame to frame.
[3] Analysis of object tracks to recognize their behaviour.
[4] Mainly the use of object Tracking is pertinent in the task of:
[5] Motion-based recognition.
[6] Automated surveillance.
[7] Video indexing.
[8] Human-computer interaction.
[9] Traffic monitoring.
[10] Vehicle navigation.
II. THE COMPLEXITIES OF TRACKING
Tracker assigns consistent labels to the tracked objects in different frames of a video. Additionally,
depending on the tracking domain, a tracker can also provide object-centric information, such as orientation,
area or shape of an object. Tracking objects can be complex due to:
[1] Loss of information caused by projection of the 3D world on a 2D image.
[2] Complex object motion and noise in images.
[3] Non rigid or articulated nature of objects, partial and full object occlusions.
[4] Complex object shapes.
[5] Scene illumination changes, and
[6] Real-time processing requirements.
By imposing constraints on the motion and appearance, objects can be tracked. Almost all tracking
algorithms assume that the object motion is smooth with no abrupt changes. One can constrain the object
2. Object Tracking Techniques For Video....
www.theijes.com The IJES Page 26
motion to be of constant velocity or a constant acceleration based on prior information. Huge knowledge about
the number and the size of objects, or the object appearance and shape, can also be used to simplify the problem.
A number of approaches for object tracking have been proposed.
III. PARAMETERS FOR OBJECT TRACKING
In a tracking scenario, an object can be defined as anything that is of interest for further analysis. For
instance, boats on the sea, fish inside an aquarium, vehicles on a road, planes in the air etc. are a set of objects
that may be important to track in a specific domain. Objects can be represented by their shapes.
In this section, we will describe the object shape representations commonly employed for tracking.
[1] Points: The object is represented by a point, that is, centroid (fig 1(a)) or by a set of points (fig 1(b)). The
point representation is suitable for tracking objects that occupy small regions in an image.
[2] Primitive geometric shapes: Object shape is represented by a rectangle, ellipse (fig 1(c), (d)) etc.
primitive geometric shapes are more suitable for representing simple rigid objects, and are also used for
tracking non rigid objects.
[3] Object silhouette and contour: Contour representation defines the boundary of an object (fig 1(g), (h)).
The region inside the contour is called the silhouette of the object (fig 1(i)). Silhouette and contour
representations are suitable for tracking complex non rigid shapes.
[4] Articulated shape models: Articulated objects are composed of body parts that are held together with
joints. For example, the human body is an articulated object with legs, hands, head, feet connected by
joints. In order to represent an articulated object, one can model the constituent parts using cylinders or
ellipses as shown in fig 1(e).
[5] Skeletal models: Object skeleton can be extracted by applying medial axis transform to the object
silhouette. This method is commonly used as a shape representation for recognizing objects. Skeleton
representation can be used to model both articulated and rigid objects (fig 1(f)).
Object representations are usually chosen according to the application domain. For tracking object,
which appear very small in an image, point representation is usually appropriate. For objects whose shapes can
be approximated by rectangle or ellipse, primitive geometric shape representations are more appropriate. For
tracking objects with complex shapes, for example, humans, a contour or silhouette based representation is
appropriate.
Fig 1, object representations (a) centroid, (b) multiple points, (c) rectangular patch, (d) elliptical patch,
(e) part-based multiple patches, (f) object skeleton, (g) complete object contour, (h) control points on
object contour, (i) object silhouette. [2]
IV. FEATURE SELECTION FOR TRACKING
Selecting the right features plays a critical role in tracking. The most desirable property of visual
feature is its uniqueness so that the objects can be easily distinguished in the feature space. In general many
tracking algorithms use these features. The details of visual features are:
[1] Colour: The apparent colour of an object is influenced primarily by two physical factors, 1) the spectral
power distribution of the illumination and 2) the surface reflectance properties of the objects. In image
processing, the RGB (red, green, blue) color space is usually used to represent color.
[2] Edges: Object boundaries usually generate strong changes in image intensities. Edge detection is used to
identify these changes. Algorithms that track the boundary of the objects usually use edge as the
representative feature.
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[3] Optical Flow: Is a dense field of displacement vectors which defines the translation of each pixel in a
region. It is computed using the brightness constraints, which assumes brightness constancy of
corresponding pixels in the consecutive frames.
[4] Texture: Texture is the measure of the intensity variation of the surface which quantifies properties such
as smoothness and regularity.
V. MOVING OBJECT DETECTION ALGORITHMS
5.1 Frame Difference:
In this method, a background image without any moving objects of interest is taken as reference image.
Pixel value for each co-ordinate (x, y) for each colour channel of the background image is subtracted from the
corresponding pixel value of the input image. If the resulting value is greater than a particular threshold value,
then that is foreground pixel otherwise background. This method is simple and easy to implement, but the
results are not accurate enough, because the changes taking place in the background brightness cause
misjudgement.
5.2 An Improved Moving Object Detection Algorithm Based On Frame Difference and Edge Detection
A combined approach is an efficient algorithm in which moving areas are detected by forming several
small blocks of edge difference image. The edge difference image is obtained by computing difference between
two images. Canny edge detecting algorithm is used to detect the edges of continuous frames. The smallest
rectangle containing the moving object can be obtained. It is possible to get the exact position of the moving
objects by calculating connected components in binary images, delete those components whose area are so
small. The improved moving object detection algorithm based on frame difference and edge detection has much
greater recognition rate and higher detection speed than several classical algorithms.
5.3 A Moving object Detection Algorithm for Smart Cameras
YongseokYoo [10] suggested a new frame differencing method for moving object detection using
signed difference and Earth Mover’s Distance (EMD). First, a signed difference image is acquired by
subtracting two consecutive frames. For each fixed block in the signed difference image, a motion pattern is
calculated by EMD. The EMD is defined as the minimum total amount of cost to move piles of earth to holes
until all the earth is moved or all the holes are filled. The neighbouring blocks are then linked to detect moving
object regions. The main idea behind this algorithm is to calculate matching costs for given directions separately
rather than to calculate exact EMD by linear programming. Here block-based motion is used to locate moving
object regions. An input image is divided into blocks of fixed size and pairing vectors are calculated for each
block. Blocks with large pairing vectors indicate that there are motions in them. By combining these blocks,
moving objects can be detected.
VI. MOVING OBJECT DETECTION FOR VIDEO SURVEILLANCE APPLICATIONS
Xiaoshi Zheng[11] proposed an automatic moving object detection algorithm based on frame
difference and region combination for video surveillance applications. Initially an automatic threshold
calculation method is used to 0obtain moving pixels of video frames. Frame difference is obtained by absolute
difference value of two frames. Moving pixels and static background pixels can be distinguished by a threshold
value. In order to make all moving pixels continuous and filter isolated pixels, moving regions are obtained by
morphological CLOSE operation. In this algorithm we have three phases i.e.
[1] moving object detection phase
[2] moving object extraction phase
[3] moving object recognition phase
6.1 Background Subtraction:
In this method, the moving regions are detected by subtracting the current image pixel-by-pixel from a
reference background image. The pixels where the difference is above a threshold are classified as foreground
otherwise background. Some morphological post processing operations are performed to reduce noise and
enhance the detected region.
6.2Real-Time Moving Object Detection for Video Monitoring System:
The method of moving object detection is based on background subtraction for real time moving
objects. It proposes a new self-adaptive background approximating and updating algorithm for moving object
detection. To obtain the correct shapes of the moving objects in every frame of the sequence, there are several
steps. The subtraction of two consecutive frames provides the image and the background model provides the
4. Object Tracking Techniques For Video....
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image. By using a temporal low-pass filter the background model is updated. The updating process is applied to
all the pixels of the model. In order to cope the sudden changes with sudden light changes and leaves swings
situation, finally AND/OR operators are applied to the images to remove tiny noise in the images. The moving
object regions can extract accurately and completely by the self-adaptive threshold segmentation method.
VII. FRAME DIFFERENCE AND BACKGROUND SUBTRACTION:
The combination of background subtraction and frame differencing can improve the detection speed and
overcome the lack of sensitivity of light changes.
Moving Object Detection Algorithm Based On Improved Background Subtraction
Lianqiang Niu[11] describes an algorithm for moving object detection based on combining of
improved background subtraction. This method can improve the detection speed and overcome the lack of
sensitivity to light changes. Considering the pixels relativity, Gaussian Mixture Model in background
subtraction is used. To extract a motion region, the differences between the current frame and its previous frame
is calculated. After getting the motion scene background by improved Gaussian Mixture Model, the foreground
image is extracted. Foreground image is obtained by subtracting the current image frame from background
image. Symmetrical differencing is used to detect the undetected regions. At each position of the pixel, the
foreground images which are achieved by using background subtraction and symmetrical differencing are
processed by a logical OR operation to obtain an accurate foreground image.
VIII. BACKGROUND UPDATING:
In background updating, the background of the selected pixels are replaced by the average of the
current and background pixels.
8.1A Moving Object Detection Algorithm Based On Colour Information
X H Fang [11] suggested an algorithm to detect moving object based on color information. This
algorithm uses a pixel and its neighbours as an image vector to represent that pixel and modeled different
chrominance component pixel as a mixture of Gaussians, and set up different mixture model of Gauss for
different YUV chrominance components. In order to make full use of the spatial information, color
segmentation and background model were combined. Simulation results show that the algorithm can detect
intact moving objects even when the foreground has low contrast with background.In the spatial object
surveillance systems, the detection of moving objects must be quick and accurate. The background changes
slowly in surveillance, so only detected objects are usually considered to be moving. Hence the background
model algorithm is always used to detect moving object. The principle of background model algorithm is to set
up statistical model of background, and then make the difference image of current image and background image
to extract moving foreground. Stauffer at al took use of Mixture of Gaussian (MOG) as the statistic model of
background [11], and every parameters of Gaussian distribution change continuously to be adapt for the gradual
change of background. The algorithm has better adaptive capability for incomplete dynamic background. The
fault of MOG is that, when foreground texture and colour are homogeneous and have low contrast with
background, the detected foreground is also not intact.
IX. CROSS CORRELATION:
Manoj S Nagmode described a method to detect and track the moving objects to detect and
track the moving objects by using Normalized Cross Correlation algorithm (NCC) [12]. In this an approach is
proposed for the detection and tracking of moving object in an image sequence. Two consecutive frames from
image sequence are partitioned into four quadrants and then the Normalized Cross Correlation (NCC) is applied
to each sub frame [13]. The sub frame which has minimum value of NCC indicates the presence of moving
object. Next step is to identify the location of the moving object. Location of the moving object is obtained by
performing component connected analysis and morphological processing. After that the centroid calculation is
used to track the moving object. Number of experiments performed using indoor and outdoor image sequences.
The results are compared with Simple Difference (SD) method. The proposed algorithm gives better
performance in terms of Detection Rate (DR) and processing time per frame.
9.1Detect and Track Moving Object Using Partitioning and Normalized Cross Correlation
Normalized cross correlation (NCC) algorithm is based on finding the cross correlation
between two consecutive frames in an image sequence. Correlation is basically used to find the similarity
between two frames. If the two consecutive frames are exactly same, then the value of Normalized cross
correlation is maximum. In that case no moving object is detected. Now suppose there is a moving object in the
image sequence, means the two consecutive frames are not exactly same, with respect to positions of the pixel
5. Object Tracking Techniques For Video....
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values. In that case the value of Normalized [12] cross correlation is less than maximum value obtained. This
concept of Normalized cross correlation is used for the detection of moving object in an image sequence.
X. CONCLUSION
Object tracking means tracing the progress of objects as they move about in visual scene.
Object tracking, thus, involves processing spatial as well as temporal changes. Certain features of those objects
have to be selected for tracking. These features need to be matched over different frames. Significant progress
has been made in object tracking. Taxonomy of moving object detection is been proposed. Algorithm based on
FD & edge detection have Higher recognition rate and higher detection speed but False detection under
complicated background. Algorithm for smart cameras have an advantage of Rejecting false motions due to
illumination changes but Falsely detecting specular reflections from moving objects. Algorithm for video
surveillance applications is Automatic and efficient in detecting moving objects but Calculations are more
complex. Performances of various object detection algorithms have also been discussed.
REFERENCES
[1] Dynamic probe window based optimization for video surveillance in home security,BhaskarKapoor and AnamikaChhabr,
Department of Information Technology, MAIT, New Delhi INDIA
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[5] Recognition of two-person interactions using a Hierarchical Bayesian Network,Sangho Park and J. K. Aggarwal. In IWVS ’03: First
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[6] Individual recognition using gait energy image, Ju Han and BirBhanu. IEEE Transactions on Pattern Analysis and Machine
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[8] Recognizing strokes in tennis videos using Hidden Markov Models,M. Petkovic, Z. Zivkovic, and W. Jonker.In Proceedings of the
IASTED International Conference Visualization, Imaging and Image Processing, Marbella, Spain, 2001.
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[10] Asymmetric Multi-phase deformable model for colon”Yong SeokYooy, Kyoung Mu Leey, Dong Yunz, and Sang UkLee,IEEE.
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[12] A Novel approach to Detect and Track Moving Object using Partitioning and Normalized Cross Correlation.”, Manoj S Nagmode,
Mrs Madhuri, A Joshi, Ashok M Sapkal.
BIOGRAPHIES
Mansi Manocha is currently doing her M-Tech degree in Electronics and Communication in Chandigarh
Engineering College, Landran, India. She received her B-Tech degree in Electronics and Communication from
Sri Sukhmani Institute of Engineering and Technology, Derabassi, Punjab, India. Her area of interest includes
biometrics, Pattern Recognition, Surveillance and Securities.
Parminder Kaur is currently an Associate Professor in ECE Department in Chandigarh Engineering College,
Landran, India. She received both her B-Tech and M-Tech degree in Electronics and Communication from Guru
Nanak Dev Engineering College, Ludhiana, Punjab, India.. Her Experience is of more than Twelve years. Her
research work includes three papers published in International Journal and eight papers in Conferences. She has
got the Best teacher award in ECE for the year 2012-2013. Her area of interest is Image Processing.