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The Study on Algorithm of Object Tracking Based on Color Feature

Author: WangJianLin
Tutor: QiaoShuang
School: Northeast Normal University
Course: Circuits and Systems
Keywords: Target tracking Color histogram mean-shift algorithm Particle filter algorithm Resampling
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 157
Quote: 5
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Abstract


Vigorously develop along with the technology of machine vision , target tracking technology has been further enhanced in terms of applications and requirements . Target detection based tracking methods have been difficult to meet the needs of the engineering practice , due to the correlation between the image sequence , the broader target recognition and associated target tracking method . Recognition - based tracking method has made a lot of achievements , but the practice shows , still does not have a universal tracking algorithm . The main purpose is to study the target tracking algorithm able to develop a robust, real-time and has a strong ability to adapt core tracking algorithm . Mean-shift algorithm is a non parametric density estimation algorithms, the good convergence target tracking and faster , but due to the nature unimodal search algorithm , so that the target tracking system performance in many cases not enough sound . Particle filter with posterior probability distribution of a set of weights of particles full description better than the mean-shift algorithm robustness due to the nature of the particle filter multimodal search , target tracking methods , but the large amount of calculation , the target tracking the real-time poor. In this thesis, the basic theory of learning research , the choice of color information by tracking the target target characteristics, mean-shift based on color feature target tracking methods and particle filter tracking method in MATLAB . Finally, the combination of two methods , convergence the particle method instead of the traditional particle filter algorithm resampling method using mean-shift algorithm adaptive method , and in the process of convergence particle particle region choose . The experiments show that the corresponding reduction of the number of particles can guarantee the same tracking accuracy significantly improved tracking speed .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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