a broad range of applications. Moving object classification in the field of video surveillance is a key component of smart surveillance software. In this paper, we have proposed reliable software with its large features for people, vehicle and object classification which works well in challenging real-world constraints, including the presence of shadows, low resolution imagery, occlusion, perspective distortions, arbitrary camera viewpoints, and groups of people. We have discussed a generic model of smart video surveillance systems that can meet requirements of strong commercial applications and also shown the implication of the software for the security purposes which made the whole system as a smart network. Smart surveillance systems use automatic image understanding techniques to extract information from the surveillance data.
The document summarizes an algorithm for object detection and tracking in moving backgrounds under different environmental conditions. The algorithm uses a discriminative learning approach to develop a more robust way of updating an adaptive appearance model. It aims to handle partial occlusions without significant drift and work well with minimal parameter tuning. The algorithm divides each frame into blocks and extracts features using a random Gaussian matrix method. A Gaussian classifier is used to get the tracking location with the highest response. The classifier is incrementally learned and updated using positive and negative samples to predict the object location in the next frame. The proposed algorithm is shown to outperform existing L1-tracker algorithms in terms of accuracy, computational efficiency, and robustness to appearance changes.
This document discusses techniques for identifying abnormal vehicle behavior in traffic videos. It begins with an abstract that outlines the goal of detecting abnormal vehicles to improve traffic safety. The introduction then provides context on video surveillance systems and their use in traffic monitoring. The document goes on to discuss specific techniques for object detection, tracking, and classification that can be used to analyze vehicle behavior and identify abnormalities. These include background subtraction, hierarchical background modeling, and classification using features like size and motion. Hidden Markov Models, neural networks, and clustering approaches are also mentioned for modeling vehicle motion and detecting anomalous events.
IRJET- Real Time Video Object Tracking using Motion EstimationIRJET Journal
The document discusses real time video object tracking using motion estimation techniques. It describes using background subtraction, thresholding, background estimation and optical flow to detect and track moving objects in video frames. Morphological operations like dilation and erosion are used for smoothing detected object regions. Dynamic thresholding and mathematical morphology help attenuate color variations from background motions while highlighting moving objects. The algorithm marks pixels as foreground if above a threshold and performs closing and removes small regions. Background is updated adaptively to prevent detection of artificial tails behind moving objects. Correlation of frames improves detection of multiple moving objects with significant contrast changes, even with poor lighting conditions.
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.
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
Abnormal activity detection in surveillance video scenesTELKOMNIKA JOURNAL
- The document presents an intelligent framework for detecting abnormal human activity in surveillance videos of an academic environment.
- The framework consists of two main processes: 1) a tracking system that identifies and tracks targets while extracting features to understand human activity, and 2) a decision system that classifies activity as normal or abnormal and triggers an alarm for abnormal activities.
- The key steps are preprocessing videos, detecting moving objects, segmenting images, extracting features, tracking targets, and classifying activities as normal (walking) or abnormal (falling, boxing, waving) using KNN. Alarms are generated for recognized abnormal activities.
Analysis of Human Behavior Based On Centroid and Treading TrackIJMER
This document discusses a video surveillance system that uses background subtraction and centroid tracking to analyze human behavior in videos. It begins with an introduction and overview of previous work on motion detection methods. It then describes the proposed system, which uses an adaptive background subtraction method to detect moving objects and extract centroid features for tracking. Experimental results show the system can detect abnormal behaviors by analyzing changes in an object's centroid movement and treading track over time. The system is able to distinguish between normal and irregular behaviors with high accuracy.
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.
The document summarizes an algorithm for object detection and tracking in moving backgrounds under different environmental conditions. The algorithm uses a discriminative learning approach to develop a more robust way of updating an adaptive appearance model. It aims to handle partial occlusions without significant drift and work well with minimal parameter tuning. The algorithm divides each frame into blocks and extracts features using a random Gaussian matrix method. A Gaussian classifier is used to get the tracking location with the highest response. The classifier is incrementally learned and updated using positive and negative samples to predict the object location in the next frame. The proposed algorithm is shown to outperform existing L1-tracker algorithms in terms of accuracy, computational efficiency, and robustness to appearance changes.
This document discusses techniques for identifying abnormal vehicle behavior in traffic videos. It begins with an abstract that outlines the goal of detecting abnormal vehicles to improve traffic safety. The introduction then provides context on video surveillance systems and their use in traffic monitoring. The document goes on to discuss specific techniques for object detection, tracking, and classification that can be used to analyze vehicle behavior and identify abnormalities. These include background subtraction, hierarchical background modeling, and classification using features like size and motion. Hidden Markov Models, neural networks, and clustering approaches are also mentioned for modeling vehicle motion and detecting anomalous events.
IRJET- Real Time Video Object Tracking using Motion EstimationIRJET Journal
The document discusses real time video object tracking using motion estimation techniques. It describes using background subtraction, thresholding, background estimation and optical flow to detect and track moving objects in video frames. Morphological operations like dilation and erosion are used for smoothing detected object regions. Dynamic thresholding and mathematical morphology help attenuate color variations from background motions while highlighting moving objects. The algorithm marks pixels as foreground if above a threshold and performs closing and removes small regions. Background is updated adaptively to prevent detection of artificial tails behind moving objects. Correlation of frames improves detection of multiple moving objects with significant contrast changes, even with poor lighting conditions.
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.
IRJET- A Review Analysis to Detect an Object in Video Surveillance SystemIRJET Journal
This document reviews techniques for detecting objects in video surveillance systems. It discusses common object detection methods like frame differencing, optical flow, and background subtraction. Frame differencing detects motion by calculating pixel differences between frames but cannot detect still objects. Optical flow estimates pixel motion between frames to detect objects. Background subtraction models the static background and detects objects by subtracting current frames from the background model. The document analyzes these techniques and their use in video surveillance applications like traffic monitoring and security. It concludes more research is needed to improve object classification accuracy and handle challenges like camera motion.
Abnormal activity detection in surveillance video scenesTELKOMNIKA JOURNAL
- The document presents an intelligent framework for detecting abnormal human activity in surveillance videos of an academic environment.
- The framework consists of two main processes: 1) a tracking system that identifies and tracks targets while extracting features to understand human activity, and 2) a decision system that classifies activity as normal or abnormal and triggers an alarm for abnormal activities.
- The key steps are preprocessing videos, detecting moving objects, segmenting images, extracting features, tracking targets, and classifying activities as normal (walking) or abnormal (falling, boxing, waving) using KNN. Alarms are generated for recognized abnormal activities.
Analysis of Human Behavior Based On Centroid and Treading TrackIJMER
This document discusses a video surveillance system that uses background subtraction and centroid tracking to analyze human behavior in videos. It begins with an introduction and overview of previous work on motion detection methods. It then describes the proposed system, which uses an adaptive background subtraction method to detect moving objects and extract centroid features for tracking. Experimental results show the system can detect abnormal behaviors by analyzing changes in an object's centroid movement and treading track over time. The system is able to distinguish between normal and irregular behaviors with high accuracy.
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.
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.
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
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.
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.
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.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
Automatic Foreground object detection using Visual and Motion SaliencyIJERD Editor
This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.
Video Surveillance Systems For Traffic MonitoringMeridian Media
The document discusses video surveillance systems for traffic monitoring. It covers object tracking techniques used in vehicle tracking systems, including background subtraction, temporal differencing, and optical flow. It also describes different vehicle detection techniques such as model-based, region-based, active contour-based, and feature-based tracking. A real-time traffic monitoring system is presented that uses feature-based tracking and camera calibration to detect, track, and group vehicles moving through the scene.
In today's competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10'ths of the law. Hence, it is imperative for one to be able to safeguard one's property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing has been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property
Applications of Image Processing and Real-Time embedded Systems in Autonomous...CSCJournals
As many of the latest technologists have predicted, Self-driving autonomous cars are going to be the future in the transportation sector. Many of the billion dollar companies including Google, Uber, Apple, NVIDIA, and Tesla are pioneering in this field to invent fully autonomous vehicles. This paper presents a literature review on some of the important segments in an autonomous vehicle development arena which touches real time embedded systems applications. This paper surveyed research papers on the technologies used in autonomous vehicles which includes lane detection, traffic signal identification, and speed bump detection. The paper focuses on the significance of image processing and real time embedded systems in driving the automotive industry towards autonomy and high security pathways.
Fast Human Detection in Surveillance VideoIOSR Journals
This document proposes a fast and efficient algorithm for tracking humans in indoor surveillance videos. It uses HOG features and correlation-based methods. Candidate frames are selected using strips along the borders to detect new objects entering the frame. HOG features are extracted only on candidate frames to reduce computation time. A correlation-based method then tracks humans between frames using the highest correlated sample window as the tracked window. Experimental results showed over 86% detection rates on test videos totalling over 6 hours, demonstrating the algorithm can detect and track humans in real-time surveillance applications.
A Video Processing based System for Counting VehiclesIRJET Journal
This document describes a video processing system for counting vehicles. The system processes video frames using discrete wavelet transform (DWT) features and a neural network. In the first phase, vehicle images are extracted from videos and used to train a backpropagation neural network to detect vehicles based on DWT features. In the testing phase, video frames are extracted and the DWT features of frames showing the detection point are input to the neural network to detect vehicles. The system was tested on videos and achieved satisfactory counting accuracy ranging from 97.9-100%. The system provides an effective way to count vehicles for applications like traffic analysis.
This document summarizes a research paper on visual pattern recognition in robotics. It discusses:
1) The paper presents a real-time visual pattern recognition algorithm to detect and recognize traffic signboards using color filtering, locating signs in images, and detecting patterns. Color filtering is the most challenging step.
2) The standard technique involves color segmentation, shape detection using templates, and specific sign detection. The presented algorithm applies a color filter to mark signboard borders, aiming to minimize detecting non-sign red colors.
3) Detection and recognition are the major steps - detection locates signs, and recognition identifies patterns to control the robot's movement accordingly.
Exploring visual and motion saliency for automatic video object extractionMuthu Samy
Sybian Technologies Pvt Ltd
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A COMPARATIVE STUDY ON HUMAN ACTION RECOGNITION USING MULTIPLE SKELETAL FEATU...mlaij
This paper proposes a framework for human action recognition (HAR) by using skeletal features from depth video sequences. HAR has become a basis for applications such as health care, fall detection, human position tracking, video analysis, security applications, etc. Wehave used joint angle quaternion
and absolute joint position to recognitionhuman action. We also mapped joint position on (3) Lie algebra and fuse it with other features. This approach comprised of three steps namely (i) an automatic skeletal feature (absolute joint position and joint angle) extraction (ii) HAR by using multi-class Support
Vector Machine and (iii) HAR by features fusion and decision fusion classification outcomes. The HAR methodsare evaluated on two publicly available challenging datasets UTKinect-Action and Florence3DAction datasets. The experimental results show that the absolute joint positionfeature is the best than other
features and the proposed framework being highly promising compared to others existing methods.
1. The document describes a method for real-time detection of moving objects based on background subtraction and its implementation on an FPGA. A static camera is used to capture video frames. The first frame is used as the reference background frame. Pixels in subsequent frames are compared to the background frame and objects are detected where pixel differences exceed a threshold.
2. The method was tested on sample surveillance videos. Background subtraction accurately detected moving objects in test videos in real-time. Future work may include identifying objects using face or palm recognition and activity recognition for visual surveillance applications.
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.
IRJET- Tracking and Recognition of Multiple Human and Non-Human ActivitesIRJET Journal
The document presents a method for tracking and recognizing multiple human and non-human activities using video surveillance. The method uses image processing techniques to extract frames from video datasets containing human activities like walking and running. A relevance vector classifier is used to classify the activities after extracting histogram of oriented gradients (HOG) features from the frames. The results show the method can detect objects like humans and cars in the background and recognize activities with high accuracy across training, testing and validation phases. Comparisons show the relevance vector classifier performs better than a support vector classifier for this task.
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.
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
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.
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.
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.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
Automatic Foreground object detection using Visual and Motion SaliencyIJERD Editor
This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.
Video Surveillance Systems For Traffic MonitoringMeridian Media
The document discusses video surveillance systems for traffic monitoring. It covers object tracking techniques used in vehicle tracking systems, including background subtraction, temporal differencing, and optical flow. It also describes different vehicle detection techniques such as model-based, region-based, active contour-based, and feature-based tracking. A real-time traffic monitoring system is presented that uses feature-based tracking and camera calibration to detect, track, and group vehicles moving through the scene.
In today's competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10'ths of the law. Hence, it is imperative for one to be able to safeguard one's property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing has been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property
Applications of Image Processing and Real-Time embedded Systems in Autonomous...CSCJournals
As many of the latest technologists have predicted, Self-driving autonomous cars are going to be the future in the transportation sector. Many of the billion dollar companies including Google, Uber, Apple, NVIDIA, and Tesla are pioneering in this field to invent fully autonomous vehicles. This paper presents a literature review on some of the important segments in an autonomous vehicle development arena which touches real time embedded systems applications. This paper surveyed research papers on the technologies used in autonomous vehicles which includes lane detection, traffic signal identification, and speed bump detection. The paper focuses on the significance of image processing and real time embedded systems in driving the automotive industry towards autonomy and high security pathways.
Fast Human Detection in Surveillance VideoIOSR Journals
This document proposes a fast and efficient algorithm for tracking humans in indoor surveillance videos. It uses HOG features and correlation-based methods. Candidate frames are selected using strips along the borders to detect new objects entering the frame. HOG features are extracted only on candidate frames to reduce computation time. A correlation-based method then tracks humans between frames using the highest correlated sample window as the tracked window. Experimental results showed over 86% detection rates on test videos totalling over 6 hours, demonstrating the algorithm can detect and track humans in real-time surveillance applications.
A Video Processing based System for Counting VehiclesIRJET Journal
This document describes a video processing system for counting vehicles. The system processes video frames using discrete wavelet transform (DWT) features and a neural network. In the first phase, vehicle images are extracted from videos and used to train a backpropagation neural network to detect vehicles based on DWT features. In the testing phase, video frames are extracted and the DWT features of frames showing the detection point are input to the neural network to detect vehicles. The system was tested on videos and achieved satisfactory counting accuracy ranging from 97.9-100%. The system provides an effective way to count vehicles for applications like traffic analysis.
This document summarizes a research paper on visual pattern recognition in robotics. It discusses:
1) The paper presents a real-time visual pattern recognition algorithm to detect and recognize traffic signboards using color filtering, locating signs in images, and detecting patterns. Color filtering is the most challenging step.
2) The standard technique involves color segmentation, shape detection using templates, and specific sign detection. The presented algorithm applies a color filter to mark signboard borders, aiming to minimize detecting non-sign red colors.
3) Detection and recognition are the major steps - detection locates signs, and recognition identifies patterns to control the robot's movement accordingly.
Exploring visual and motion saliency for automatic video object extractionMuthu Samy
Sybian Technologies Pvt Ltd
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A COMPARATIVE STUDY ON HUMAN ACTION RECOGNITION USING MULTIPLE SKELETAL FEATU...mlaij
This paper proposes a framework for human action recognition (HAR) by using skeletal features from depth video sequences. HAR has become a basis for applications such as health care, fall detection, human position tracking, video analysis, security applications, etc. Wehave used joint angle quaternion
and absolute joint position to recognitionhuman action. We also mapped joint position on (3) Lie algebra and fuse it with other features. This approach comprised of three steps namely (i) an automatic skeletal feature (absolute joint position and joint angle) extraction (ii) HAR by using multi-class Support
Vector Machine and (iii) HAR by features fusion and decision fusion classification outcomes. The HAR methodsare evaluated on two publicly available challenging datasets UTKinect-Action and Florence3DAction datasets. The experimental results show that the absolute joint positionfeature is the best than other
features and the proposed framework being highly promising compared to others existing methods.
1. The document describes a method for real-time detection of moving objects based on background subtraction and its implementation on an FPGA. A static camera is used to capture video frames. The first frame is used as the reference background frame. Pixels in subsequent frames are compared to the background frame and objects are detected where pixel differences exceed a threshold.
2. The method was tested on sample surveillance videos. Background subtraction accurately detected moving objects in test videos in real-time. Future work may include identifying objects using face or palm recognition and activity recognition for visual surveillance applications.
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.
IRJET- Tracking and Recognition of Multiple Human and Non-Human ActivitesIRJET Journal
The document presents a method for tracking and recognizing multiple human and non-human activities using video surveillance. The method uses image processing techniques to extract frames from video datasets containing human activities like walking and running. A relevance vector classifier is used to classify the activities after extracting histogram of oriented gradients (HOG) features from the frames. The results show the method can detect objects like humans and cars in the background and recognize activities with high accuracy across training, testing and validation phases. Comparisons show the relevance vector classifier performs better than a support vector classifier for this task.
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
IRJET-Real-Time Object Detection: A SurveyIRJET Journal
This document provides an overview of real-time object detection techniques. It discusses several challenges in detecting objects including illumination variation, moving object appearance changes, abrupt motion, occlusion, shadows, and problems related to cameras. The document then reviews several existing object detection methods and algorithms. These include techniques using color segmentation, edge tracking, shape context features, image segmentation, and support vector machines or k-nearest neighbor classifiers applied to features like GIST or SIFT. The goal of the literature review is to analyze different object recognition and segmentation approaches that could be applied for real-time object detection.
Object Tracking System Using Motion Detection and Sound DetectionAM Publications,India
Visual monitoring activities using cameras automatically without human intervention is a big and challenging problem so we need automatic object tracker system. This paper presents a new object tracking system in Real time that systematically combines both motion detection and sound detection. In this system detect motion as well as sound in a real time and if lack of security it is also give alert message through alarm. The proposed method is excellent in real-time performance because it detect the moving objects efficiently and accurately form the video recorded by a shaking camera with changing background and noises.
IRJET - Real-Time Analysis of Video Surveillance using Machine Learning a...IRJET Journal
This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
Intelligent Video Surveillance System using Deep LearningIRJET Journal
This document discusses an intelligent video surveillance system using deep learning. It proposes a framework that first detects abnormal human activity in video streams using an effective CNN model. It then tracks detected individuals throughout the video using an ultra-fast object tracker. Feature extraction is performed on consecutive frames using a CNN, and a deep learning model is trained to recognize and detect activities based on temporal changes in frames. The system aims to allow for quick abnormal activity detection with low computational complexity compared to other methods.
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.
IRJET- Border Security using Computer VisionIRJET Journal
1. The document describes a computer vision system for border security that uses image processing techniques to automatically detect, track, and destroy targets.
2. The system uses a video camera to capture images that are processed on a computer using MATLAB. Targets can be automatically tracked using image processing algorithms or manually selected by a user.
3. Once a target is selected, the microcontroller controls a mounted gun to track and potentially shoot the target. The goal of the system is to secure borders automatically while reducing human effort.
IRJET- Real-Time Object Detection System using Caffe ModelIRJET Journal
This document discusses a real-time object detection system using the Caffe model. The authors used OpenCV, Caffe model, Python and NumPy to build a system that can detect objects like humans and vehicles in images and videos. It discusses how deep learning techniques like convolutional neural networks can be used for tasks like object localization, classification and feature extraction. Specifically, it explores using the Caffe framework to implement real-time object detection with OpenCV by accessing the webcam and applying detection to each frame.
Development of Human Tracking in Video Surveillance System for Activity Anal...IOSR Journals
This document discusses the development of a human tracking system for video surveillance. It proposes a three step process: 1) detecting moving objects through background subtraction and optical flow segmentation, 2) tracking detected humans across frames while handling occlusion, and 3) analyzing activities to trigger alerts for abnormal behaviors. Previous research on human detection, tracking, and occlusion handling is also reviewed. The overall architecture is presented with each step - detection, tracking, and activity analysis - broken down in more detail.
Person Acquisition and Identification ToolIRJET Journal
The document proposes a facial recognition system using CCTV video to identify individuals and generate timestamp data on their presence. It involves three steps: 1) face detection on video frames, 2) super resolution to standardize face sizes, and 3) face recognition using a Siamese network to identify known and new identities with one-shot learning. The system aims to reduce time spent reviewing surveillance footage for law enforcement. It analyzes existing research on low-resolution face recognition, pedestrian detection, and proposes its pipeline as a solution to semi-automate target individual tracking from video data through facial matching and timestamps.
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.
IRJET- Intruder Detection System using Camera with Alert ManagementIRJET Journal
This document describes a proposed intruder detection system using a camera. The system would work as follows:
1. A camera would continuously capture video frames and an image processing processor would compare the latest frame to a static threshold frame to detect differences.
2. If a difference is detected above a certain threshold using a sum of absolute differences (SAD) algorithm, it would indicate a potential intruder.
3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
The goal is to create an affordable intruder detection system that can detect intruders and raise alerts, providing security for places when unattended.
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).
Abandoned Object Detection Based on Statistics for Labeled RegionsIRJET Journal
This document summarizes an algorithm for detecting abandoned objects in video surveillance footage. It first preprocesses the video footage by converting it to grayscale and applying background subtraction and morphological operations to extract foreground objects. It then uses blob analysis to find properties of detected objects like area, centroid, and bounding box. Static foreground regions that remain for a threshold period of time are identified as potential abandoned objects. The algorithm draws rectangles around detected objects and displays the video with abandoned objects highlighted to identify them. It aims to provide an effective yet simple method for abandoned object detection in public spaces.
Bibliometric Analysis on Computer Vision based Anomaly Detection using Deep L...IRJET Journal
This document discusses a bibliometric analysis of research on computer vision-based anomaly detection using deep learning. It analyzes 385 documents from 2012-2021 that used keywords related to anomaly detection, the UCF-Crime dataset, image processing, artificial intelligence, and deep learning. The analysis examines publication types, contributing countries and authors, organizational trends, and citations to understand research progress in this area. It finds growing research from 2018-2021 and identifies opportunities for future work in smart surveillance systems using anomaly detection.
Real Time Moving Object Detection for Day-Night Surveillance using AIIRJET Journal
This document presents a project to develop a web application for real-time moving object detection for both day and night surveillance using artificial intelligence. The application can detect objects in images, videos, and real-time video streams from a user's device. It implements computer vision techniques using OpenCV for image processing. A YOLOv4 deep learning model is trained on a dataset containing dark images to enable nighttime object detection. The trained model is deployed as a web app using Streamlit and hosted on Heroku to be accessible across different devices and operating systems. The project aims to provide a solution for various object detection and image processing tasks.
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Event-Handling Based Smart Video Surveillance System
1. Fadhlan Hafiz, A.A. Shafie, M.H. Ali, Othman Khalifa
International Journal of Image Processing (IJIP), Volume (4): Issue (1) 24
Event-Handling Based Smart Video Surveillance System
Fadhlan Hafiz fadhlan_hafiz@yahoo.co.uk
Faculty of Engineering
International Islamic University Malaysia
53100 Kuala Lumpur, Malaysia
A.A. Shafie aashafie@iiu.edu.my
Faculty of Engineering
International Islamic University Malaysia
53100 Kuala Lumpur, Malaysia
M.H. Ali hazratalidu07@yahoo.com
Faculty of Engineering
International Islamic University Malaysia
53100 Kuala Lumpur, Malaysia
Othman Khalifa khalifa@iiu.edu.my
Faculty of Engineering
International Islamic University Malaysia
53100 Kuala Lumpur, Malaysia
Abstract
Smart video surveillance is well suited for a broad range of applications. Moving
object classification in the field of video surveillance is a key component of smart
surveillance software. In this paper, we have proposed reliable software with its
large features for people and object classification which works well in challenging
real-world constraints, including the presence of shadows, low resolution
imagery, occlusion, perspective distortions, arbitrary camera viewpoints, and
groups of people. We have discussed a generic model of smart video
surveillance systems that can meet requirements of strong commercial
applications and also shown the implication of the software for the security
purposes which made the whole system as a smart video surveillance. Smart
surveillance systems use automatic image understanding techniques to extract
information from the surveillance data and handling events and stored data
efficiently.
Keywords: Smart system, Human detection, Video surveillance, Object classification, Event handling
1. INTRODUCTION
Many applications of smart camera networks relate to surveillance and security system. Although
applications of smart camera networks are fairly broad, they rely on only a few elementary
estimation tasks. These tasks are object detection, object localization, target tracking, and object
classification. Many surveillance methods are based on a general pipeline-based framework;
2. Fadhlan Hafiz, A.A. Shafie, M.H. Ali, Othman Khalifa
International Journal of Image Processing (IJIP), Volume (4): Issue (1) 25
moving objects are first detected, then they are classified and tracked over a certain number of
frames, and, finally, the resulting paths are used to distinguish “normal” objects from “abnormal”
ones. In general, these methods contain a training phase during which a probabilistic model is
built using paths followed by “normal” objects [1].Smart video surveillance systems achieve more
than motion detection. The common objectives of smart video surveillance systems are to detect,
classify, track, localize and interpret behaviors of objects of interest in the environment [2]. Some
countries have implemented cabin video-surveillance systems which require pilot’s involvement in
their operation as there is no intelligence built in them. Moreover, after 20 minutes of surveillance,
in all such non-automated vigilance systems, the human attention to the video details degenerate
into an unacceptable level and the video surveillance becomes meaningless. Thus there is an
increasing demand for intelligent video surveillance systems with automated tracking and alerting
mechanism [3].
Relevant work in this area include shape-base techniques which exploit features like size,
compactness, aspect ratio, and simple shape descriptors obtained from the segmented object[4,
5].The smart camera delivers a new video quality and better video analysis results, if it is
compared to existing solutions. Beside these qualitative arguments and from a system
architecture point of view, the smart camera is an important concept in future digital and
heterogeneous third generation visual surveillance systems [6]. Similarly, visual object
classification is a key component of smart surveillance systems. The ability to automatically
recognize objects in images is essential for a variety of surveillance applications, such as the
recognition of products in retails stores for loss prevention, automatic identification of vehicle
license plates, and many others.
In this paper, we address a simplified two-class object recognition problem: given a
moving object in the scene, our goal is to classify the object into either a person (including groups
of people) or a vehicle. This is a very important problem in city surveillance, as many existing
cameras are pointing to areas where the majority of moving objects are either humans or
vehicles. In our system, this classification module generates metadata for higher-level tasks, such
as event detection (e.g., cars speeding, people loitering) and search (e.g., finding red cars in the
video). We assume static cameras, and thus benefit from background modeling algorithms to
detect moving objects [7]. Wolf et al. identified smart camera design as a leading-edge
application for embedded systems research [8].In spite of these simplifications, the classification
problem still remains very challenging, as we desire to satisfy the following requirements:
(a) Real-time processing and low memory consumption
(b) The system should work for arbitrary camera views
(c) Correct discrimination under different illumination conditions and strong shadow
effects
(d) Able to distinguish similar objects (such as vehicles and groups of people).
Our approach to address these issues consists of three elements [7]:
(a) Discriminative features,
(b) An adaptation process, and
(c) An interactive interface
We also have defined 3 challenges that need to be overcome:
The multi-scale challenge
This is one of the biggest challenges of a smart surveillance system. .Multi-scale techniques open
up a whole new area of research, including camera control, processing video from moving object,
resource allocation, and task-based camera management in addition to challenges in
performance modeling and evaluation.
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The contextual event detection challenge
This challenge is mostly on using knowledge of time and deployment conditions to improve video
analysis, using geometric models of the environment and other object and activity models to
interpret events, and using learning techniques to improve system performance and detect
unusual events.
The large system deployment challenge
It has several challenges include minimizing the cost of wiring, meeting the need for low-power
hardware for battery-operated camera installations, meeting the need for automatic calibration of
cameras and automatic fault detection, and developing system management tools.
2. METHODOLOGY
2.1 Camera Selection
Since the multi-scale challenge incorporates the widest range of technical challenges, we present
the generic architecture of a multi-scale tracking system. The architecture presented here
provides a view of the interactions between the various components of such a system. We
present the concepts that underlie several of the key techniques, including detection of moving
objects in video, tracking, and object classification..
Selections of the surveillance features are very important for a smart surveillance
software or smart surveillance network. Camera and its components selection depends on the
users. We have classified the camera based on the following criteria summarized in Table 1 and
our selected hardware in Table 2.
Table 1: Classification of the camera
Table 2: The hardware used for our system
2.2 Camera Placement
We have developed our software to detect the movements and display features on the monitor as
we want to view. A way to use the sun positions to determine the focal length, zenith and azimuth
angles of a camera. The idea is to minimize the re-projection error, that is, minimize the distance
between the sun labels and the sun position projected onto the image plane. If we have several
observations, we can know the solution that gives the best data in a least-squares sense as in the
following
Area Complexity Number of
Cameras
Environment Types of Camera
Small Large Simple Complex Single Multiple Day/Bright Night/Dark IP based Smart
Components Description Quantity
1.Outdoor Box Camera 1/3" Sony Super HAD, 380TVL / 0lx / 3.6mm / 22IR LEDs /
12VDC
3
2.Dome Camera 1/3" Sony Super HAD 1
3. DVR Card 4 Channel 3rd Party DVR Card 1
4. Cable Lay coaxial cable 50 ft
5.DVR DVR configuration 1
7.Main Server High Speed CPU 1
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Where, Pi is the projection operator. Our goal is to find the camera parameters (fc, θc, Φc) that
will best align the sun labels pi with the rotated sun position. The following equation is used to find
those parameters and summarized in Table 3:
Name Symbol Default Value
Focal Length fc 1000 px
Number of
Image
N 20
Camera Zenith θc 90
Camera Azimuth Φc 0
Table 3: Necessary camera parameters
Camera positioning is very important for clear video image. Our test camera positioning is shown
in the following figures. Camera angles are very important parameters to be considered.
Depending on the application we can choose either 45 degree or 90 degree angle’s camera.
The focus point will determine the area under surveillance. Figure 1 and 2 bellow shows the front
view and side view of the testing area. In this area we used four different cameras.1 is dome
camera and the rest 3 are box cameras.
Figure 1 : Front view of the test area Figure 2 : Side view of the test area
2.3 Smart camera video sequences handling
Figure 3 shows the internal structure of our smart surveillance system. From the figure we can
see how it works in the practical field. First the video will be recorded and then will be saved in
the specific destination in hard drive, next it will use the object detection algorithm, after that it will
follow the multi object tracking and classification method. Using this method we can sort the types
of the objects detected by the camera. Finally Event classification will be used to index the data in
the index storage from where we can retrieve the active object efficiently.
2.4 PC and software selection
The PC used for this project is Intel Quad Core Q9550, 2.83GHz with 4GB of memory and the
software used for the system development are:
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• Windows Vista.
• Microsoft Visual Studio 2008
• SQL Server Database
• AForge Image Processing Library (open source)
Figure 3: Overall internal structure of smart surveillance system
3. DETECTION, EVENT CLASSIFICATION AND DATA STORAGE
3.1 Detection algorithm
The overall human detection technique proposed in this system is discussed in details in [9] and it
can be classified into two parts, (1) Image Pre-processing and (2) Segmentation. Image pre-
processing includes frame processing, foreground segmentation, and binarization. While
Segmentation includes Shadow removal, morphological operation, noise removal, and size
filtering. The overall steps employed are summarized in Figure 4 and also illustrated with example
in Figure 5.
Figure 4: Human detection process flow Figure 5
Graphical overview
3.2 Event classification
This classification system classifies the events that occur within a space that may be monitored
by one or more cameras. The classification and associated information data processing are
carried out in real-time. The following is descriptions of few elements in event classification:
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• Event: An event is defined as any detection, movement or event that occurs within the
camera’s secured area.
• Event ID: This is a unique number which identifies a specific event. Main index to Video
ID and Human ID. Used in Indexing and Data Retrieval
• Time: Time at which the event occurs.
• Location: Location where the event occurs
• Video ID: This is a unique number which identifies a specific video recorded active
camera and is the index to the physical video storage information.
• Human ID: This is a unique number which identifies a specific human who appears within
camera focus area. Index to the image, appearance information, and tracking data
3.3 Data Storage System
The storage system in this surveillance system comprises of two components: (a) physical
storage and (b) database. Video data from the cameras are saved into the hard disk and this falls
into physical storage category. The video files are saved in frames, and not in usual video format
like .wmv or .avi format. This is because the system, which is developed in .NET framework,
cannot save the video data while analyzing the video streams for detection purpose. This feat is
applicable in COM but not in .NET. As a solution, we copy the image frame, one at a time, and
save the images on the hard disk just before the detection class processes the images. These
images are saved in JPEG format. All the data associated to that video such as the physical
location of the images, the time stamp, duration, and frame speed (fps), are saved in the video
database and each video is assigned a unique ID as the key for that information. This information
is saved in the database using SQL Server Database.
3.4 Data Indexing and Retrieval
This is the core and the brain of this surveillance system. Those IDs which were assigned earlier
in real-time, such as Event ID, Video ID and Human ID are used to index all the data in the
database and for automated data retrieval when requested by the user. Every ID in this system is
linked to each other so that user can access all necessary information like human appearance,
time, track, and video data in one comprehensive and inter-related search. The flow of Data
Indexing and Retrieval is illustrated in Figure 6. Once process objects are identified, the data
extracted from objects are stored together with the unique ID which indexed earlier into database.
Then the data retrieval will handle the request for data using the assigned ID as the key for
retrieval of data from database and then retrieve all the associated data.
Figure 6 : Data Indexing and Retrieval process flow
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4. SYSTEM OVERVIEW
Our developed system gives more advance result compared to the existing camera system. It has
varieties of displaying options which will easily help to analyze data in any time in the region of
the cameras. The functions can be divided into 5 categories as follows:
1. Surveillance Mode
In this mode, user can connect to all available cameras, and change the surveillance settings for
each camera, such as motion detection, human detection, security settings and secured
parameters. This mode also enables administrative control such as system lock, password
management and profile selection. It summarizes all the running components and the result of
each component such as number of events, number of human detected, camera frames per
second, and intruder detection. It also has full screen mode. This mode is illustrated in Figure 7
and Figure 8.
Figure 7: Surveillance in windowed mode Figure 8: Surveillance in full screen
2. Core Databases
All events and videos are saved into the hard disk and their details and locations are indexed into
the event and video database. This database will keep track of any object such as human,
detected by the surveillance systems and link the events to corresponding video. Human and
video database are shown in Figure 9 and 10 respectively. This will enable the user to easily
search for any event in interest such as human at specific time and place automatically without
the hassle of looking through terabytes of video data.
3. Advanced Search
Advanced Search is a sophisticated search engine for searching any event, human or object at
any time and place. The resulting event from this search can be used to directly open the
corresponding video in playback mode or to view the track of the requested human. Each event
bears a unique ID and unique time stamp so that a user can directly access the video of the event
using the Data Indexing and Retrieval system discussed earlier. Advanced Search with human
images result is illustrated in Figure 11
4. Playback Mode
Playback Mode as in Figure 12 can carry out several video analysis such as automated video
searching according to time constraints. It has playback feature from normal surveillance system
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such as play, pause, stop, fast forward, and rewind with adjustable magnitude. The user can
open up to maximum 4 video at a time where each video window have the same time stamp but
corresponds to different camera locations.
Figure 9: Human Database Figure 10: Video Database
Figure 11: Advanced Search Figure 12: Playback Mode
5. Video and Track Analysis
Video Analysis is linked to Advanced Search and it handles the playback of the video containing
the event searched in Advanced Search. Any video opened in this mode will list all the events
contained in the video as indexed by the system beforehand. User can click any event listed to go
directly to the video at the time it happens. The video controls of this mode are much less the
same with Playback Mode. This mode is illustrated in Figure 13. While Track Analysis enables
user to view previously recorded track of any human that appears in the video. This mode is
illustrated in Figure 14.
9. Fadhlan Hafiz, A.A. Shafie, M.H. Ali, Othman Khalifa
International Journal of Image Processing (IJIP), Volume (4): Issue (1) 32
Figure 13: Video Analysis Figure 14: Track Analysis
5. RESULT AND DISCUSSION
Our developed software works very efficiently with the real time video surveillance system. The
software is a great tool to classify and track the movement of any object under the camera’s
secured area. The software has details display mode like time, place, human or non-human, how
many object, trajectory path of the moving object, video retrieval using Data Indexing and
Retrieval, playing past videos from the hard drive and so on. The details of those features have
been discussed in details in the system overview section. This software is based on the following
key video analysis technologies:
• Human and Object Detection: This software can detect moving human and/or objects in a
video sequence generated by a static camera. The detection techniques are invariant to
changes in natural lighting, reasonable changes in the weather, distraction movements
and camera shake. Several algorithms are available in this software including adaptive
background subtraction with healing which assumes a stationary background and treats
all changes in the scene as objects of interest and salient motion detection [10] which
assumes that a scene will have many different types of motion, of which some types are
of interest from a surveillance perspective. The result of human detection is illustrated in
Figure 15
• Human and Object Tracking: This software can track the position of multiple objects as
they move around a space that is monitored by a static camera as illustrated in Figure 16.
• Object Classification: This software uses various properties of an object especially
human including shape, size and movement to assign an event and object type label to
the objects. Our system fulfills the following criteria for Advanced Search.
Searching capability and smart indexing of data made this software handles data efficiently and
ease the management of large video data. The searching itself comprises of combinations of
logics as follows:
• Search by Event Type retrieves all event matches the requested type
• Search by Time retrieves all events that occurred during a specified time interval.
• Search by Location retrieves all objects within a specified area in a camera.
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• Search by Image retrieves all images of human or objects that has appeared within the
camera’s secured area.
• Joint Search combines one or more of the above criteria as specified by the user
Figure 15: Result of human detection carried out by this system
Figure 16: Multiple human tracking
6. CONCLUSION AND FUTURE WORKS
We have presented our smart video surveillance software for the surveillance purposes. Also we
have introduced a generic smart video surveillance systems model, which relates the computer
vision algorithms. All these methods have been linked with the smart surveillance system. From
the practical point of view we found the developed software is more effective compared to the
traditional surveillance system as well as it has a details display mode which helps us to track the
moving object in an easier way and smart data handling for indexing of massive video data.
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Future Works: The event classification and Data Indexing and Retrieval system would be
improved further to enable wider search criteria to be implemented such as human/object specific
appearance search and also to enhance the detection subsystem to be able to detect more
objects. The system also will be improved to be able to classify human behavior from human
body posture, movement speed and frequency of appearances. Moreover, the partial body
occlusion is the main drawback from using shape-based human detection in this detection class
as the system cannot determine correctly the boundary of human body in case of occlusion. But
we will overcome this problem using head detection to correctly determine the number of human
presents in the scene and therefore can locate the bounding boxes accurately based on human-
shape model. The code for the software will also be revised to implement multi-threading for
better performance with multi-core processors.
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