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Research on Object Detection and Tracking Algorithms in Visual Surveillance

Author: ZhangBo
Tutor: ShenZuoJing
School: Jilin University
Course: Applied Computer Technology
Keywords: Visual Surveillance Target detection Target tracking Gaussian mixture model Kalman filter Particle Filter
CLC: TP277
Type: Master's thesis
Year: 2010
Downloads: 156
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Abstract


Visual surveillance , target detection and target tracking algorithm has long been the research focus of the direction of machine vision . It involves knowledge of image processing , pattern recognition , artificial intelligence , data fusion , mathematical modeling , multimedia technology , neural theory , biology and many other areas , and has broad application prospects and important research significance . The mainstream of current visual surveillance algorithm a lot of basic research , including the Gaussian mixture model ( GMM ) , kernel density estimation (KDE) , the image moment theory , the Kalman filter and particle filter . The main line of text to the two major problems of target detection and target tracking expand specific work arrangements are as follows : 1 . Target detection problems : First, a brief review of the background subtraction and frame difference method , and on this basis , the use of a novel based targeting dynamic background modeling method to build a model of the scene in the background . Then a detailed analysis of Gaussian mixture model (GMM) and its parameter update algorithm , and improved background learning rate , more efficient , more accurate background modeling . Target tracking problems : First introduced how to use the kernel density estimation established target color model tracking method based on deterministic theory , discuss two the mainstream algorithms MeanShift with CamShift tracking method based on the statistical theory , the main discussion the Kalman filter and particle filter . Elaborate on the mathematical model of the algorithm , supplemented by a large number of experiments , in order to illustrate the feasibility of the method . The experimental results show that the background modeling GMM model is a good robustness of the method , in the target tracking in the field , the particle filter is able to provide better tracking .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Monitoring, alarm,fault diagnosis system
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