Research On Multiple Object Tracking Of Vision Surveillance

Multi-object tracking is a hot research field in video surveillance. Particularly, inrecent years, video surveillance system has played an increasing important role, whichis widely used in homes, car parks, public places, banks and some other places for real-time monitoring. In this paper, we present a detection and tracking integrated videosurveillance system that is used to monitor the outdoor or indoor scenes occasions witha fixed color cameras. This system is able to automatically track varying number oftargets and automatically complete the initialization and termination of the track.Based on the integration of motion detection and object tracking, our video surveil-lance system is divided into four parts: motion detection module, clumps detectionmodule, tracking module and the trajectory generated modules. The main contents ofthis thesis are as follows:Motion detection part: first of all, we analysis the codebook based backgroundsubtraction algorithm in detail. We assume that pixels follow a Gaussian distribu-tion i the time domain, according to statistics and human vision system, and redesignthe codebook, brightness distorition, update rules and etc. based on the original ap-proach. So that it can detec a more complete foreground object. Secondly, we deeplyconstrue Bayesian classification based background subtraction algorithm. Using thecolor-space model in the codebook algorithm, we propose a new thresholding method,which is helpful for removing the moving shadows to some extent.Target tracking part: based on motion detection we propose a framework to ad-dress multi-object tracking problem, that is to decompose multi-object tracking taskinto the combination of multiple single-object tracking tasks. On the one hand, thesingle-object tracking problem could be viewed as a optimal state estimation problem.We study how to utilize particle filter for multi-target tracking in detail. Accordingly, we describe the object tracking related algorithms and implementation. In order toimprove tracking accuracy and efficiency, we propose to use a combination of colorand motion feature as the likelihood function and to use MCMC after particle re-sampling.On the other hand, the single-object tracking problem could also be viewedas a classification problem. Then we study how to use On-line Boosting to achieveobject tracking, and present the analysis and implementation of On-line Boosting al-gorithm, Absolute Haar features, Haar-like features, the weak classifier design, On-line Boosting Tracking processing. Considering the distance, scale, speed and motiondirection of tracker-observation pair would have an impact on the match degree oftracker-observation pair, we propose an improvement on the traditional global near-est neighbor matching function, thereby increase the accuracy and robustness of dataassociation.Based on the above, we build a video surveillance platform on PC, which inte-grates a variety of algorithms and technique, and execute a large number of experi-ments and analysis in indoor and outdoor environment. Experimental results show theeffectiveness of our system.