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Algorithm Research on Moving Object Detection of Surveillance Video Sequence  [PDF]
Kuihe Yang, Zhiming Cai, Lingling Zhao
Optics and Photonics Journal (OPJ) , 2013, DOI: 10.4236/opj.2013.32B072
Abstract:
In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.
Moving Object Tracking Techniques: A Critical Review  [PDF]
Sandeep Kumar Patel,Agya Mishra
Indian Journal of Computer Science and Engineering , 2013,
Abstract: Moving Object Tracking is one of the challenging problems in the field of computer vision, surveillance, traffic monitoring, video compression etc. The goal of object tracking is to locate a moving object in consecutive video frames. Normally a video tracking system combines three stages of data treating; object extraction, objectrecognition & tracking, and decisions about activities. This paper presents a critical review of various video object tracking techniques like point tracking, kernel tracking and Silhouette tracking algorithms. Comparison of all the techniques concludes the better approach for its future research.
Video Based Moving Object Tracking by Particle Filter  [PDF]
Md. Zahidul Islam,Chi-Min Oh,Chil-Woo Lee
International Journal of Signal Processing, Image Processing and Pattern Recognition , 2009,
Abstract: Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Most of the existing algorithms are able to track only inpredefined and well controlled environment. Some cases, they don’t consider non-linearity problem. In our paper, we develop such a system which considers color information, distance transform (DT) based shape information and also nonlinearity. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. We examine the difficulties of video based tracking and step by step we analyze these issues. In our firstapproach, we develop the color based particle filter tracker that relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop this observation system. Secondly, wedescribe a new approach for moving object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed images. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Finally, inthis paper we illustrate how our system is improved by using both these two cues with non linearity.
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios  [cached]
Quast Katharina,Kaup André
EURASIP Journal on Image and Video Processing , 2011,
Abstract: A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations.
Moving Object Tracking Using Kalman Filter  [PDF]
Hitesh A Patel,Darshak G Thakore
International Journal of Computer Science and Mobile Computing , 2013,
Abstract: In the field of security automated surveillance systems are very useful. Surveillance system can beused to detect and track the moving objects. First phase of the system is to detect the moving objects in thevideo. Second phase of the system will track the detected object. In this paper, detection of the moving objecthas been done using simple background subtraction and tracking of single moving object has been doneusing Kalman filter. The algorithm has been applied successfully on standard surveillance video datasets ofCAVIAR [6] and PETS [7]. The videos used for testing have been taken using still cameras, which arelocated in indoor as well as outdoor environment having moderate to complex environments.
Robust Feedback Zoom Tracking for Digital Video Surveillance  [PDF]
Tengyue Zou,Xiaoqi Tang,Bao Song,Jin Wang,Jihong Chen
Sensors , 2012, DOI: 10.3390/s120608073
Abstract: Zoom tracking is an important function in video surveillance, particularly in traffic management and security monitoring. It involves keeping an object of interest in focus during the zoom operation. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the so-called “trace curve”, which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. The main task of a zoom tracking approach is to accurately estimate the trace curve for the specified object. Because a proportional integral derivative (PID) controller has historically been considered to be the best controller in the absence of knowledge of the underlying process and its high-quality performance in motor control, in this paper, we propose a novel feedback zoom tracking (FZT) approach based on the geometric trace curve estimation and PID feedback controller. The performance of this approach is compared with existing zoom tracking methods in digital video surveillance. The real-time implementation results obtained on an actual digital video platform indicate that the developed FZT approach not only solves the traditional one-to-many mapping problem without pre-training but also improves the robustness for tracking moving or switching objects which is the key challenge in video surveillance.
3D Real Time Tracking and Recognization of Moving object using Kalman Filter
Sachin Kaushik
International Journal of Engineering Research , 2014,
Abstract: This paper demonstrate a technique related to video surveillance system improving the Future security systems. The main objective of this paper is to increase efficiency of moving object detection and tracking using 3D model. The method used in this paper is detection in video surveillance system then tracking the object in the scene. Detection of moving object subtly/ accurately is a challenging task especially in 2D tracking. Limitation can be overcome in 3D Tracking. In contrast of 3D tracking it will require high level of application that formulate the location, Shape of every object in every frame having two images producing with two different cameras situated at certain angular distance apart from each other. This paper presents the study on the implementation of Matlab using Kalman tracker in a feedback configuration based on moving object, detection algorithm, image processing technique like filtering, extraction, segmentation, conversion, adding, multiplying etc
Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation  [cached]
Miller O,Averbuch A,Navon E
EURASIP Journal on Image and Video Processing , 2008,
Abstract: The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour's junctions are derived. These junctions are the unique a€ signaturea€ of the tracked object. Junctions from two consecutive frames are matched. The junctions' motion is estimated using RAG edges in consecutive frames. Each pair of matched junctions may be connected by several paths (edges) that become candidates that represent a tracked contour. These paths are obtained by the -shortest paths algorithm between two nodes. The RAG is transformed into a weighted directed graph. The final tracked contour construction is derived by a match between edges (subcurves) and candidate paths sets. The RAG constructs the tracked contour that enables an accurate and unique moving object representation. The algorithm tracks multiple objects, partially covered (occluded) objects, compounded object of merge/split such as players in a soccer game and tracking in a crowded area for surveillance applications. We assume that features of topologic signature of the tracked object stay invariant in two consecutive frames. The algorithm's complexity depends on RAG's edges and not on the image's size.
Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation  [cached]
O. Miller,A. Averbuch,E. Navon
EURASIP Journal on Image and Video Processing , 2008, DOI: 10.1155/2008/328052
Abstract: The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour's junctions are derived. These junctions are the unique ¢ € signature ¢ € of the tracked object. Junctions from two consecutive frames are matched. The junctions' motion is estimated using RAG edges in consecutive frames. Each pair of matched junctions may be connected by several paths (edges) that become candidates that represent a tracked contour. These paths are obtained by the k-shortest paths algorithm between two nodes. The RAG is transformed into a weighted directed graph. The final tracked contour construction is derived by a match between edges (subcurves) and candidate paths sets. The RAG constructs the tracked contour that enables an accurate and unique moving object representation. The algorithm tracks multiple objects, partially covered (occluded) objects, compounded object of merge/split such as players in a soccer game and tracking in a crowded area for surveillance applications. We assume that features of topologic signature of the tracked object stay invariant in two consecutive frames. The algorithm's complexity depends on RAG's edges and not on the image's size.
IMPLEMENTATION OF OBJECT TRACKING SYSTEM USING REGION FILTERING ALGORITHM BASED ON SIMULINK BLOCKSETS
DR.P.SUBASHINI,MS.M.KRISHNAVENI,MR. VIJAY SINGH
International Journal of Engineering Science and Technology , 2011,
Abstract: Video tracking is the process of locating a moving object in time that is visualized by camera and are widely used in surveillance, animation and robotics Tracking describes the process of recording movement and translating that movement onto a digital model. The set of constraints that produce the most accurate tracking is the one that describes better the action performed. The key difficulty in video tracking is to associate target locations in consecutive video frames, especially when the objects are moving fast relative to the frame rate. Here, a video tracking system is been employed in which motion model describes how the image of the target might change for different possible motions of the object to track. The role of the tracking algorithm adopted for this system is to analyze the video frames to estimate the motion parameters. These parameters characterize the location of the target. In this research, three features are been extracted from each moving objects such as centroid, area, average luminance. Finally the similarity function is applied to tracking and the attempt proves that the chosen method has good performance under dynamic circumstances for real time tracking. Simulink is integrated with MATLAB to build a model for object tracking and data transfer is easily handled between the programs. The Simulink based customizable framework is designed for rapid simulation, implementation, and verification of video and image processing algorithms and systems.
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