Mean Shift tracking algorithm based on background optimization

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Mean Shift tracking algorithm based on background optimization

Received:2008-11-03 Revised:2009-01-03 Online:2009-04-01 Published:2009-04-01
Contact: Wei LIU

基于背景优化的Mean Shift目标跟踪算法

柳伟罗以宁孙南

四川大学，计算机学院四川大学计算机学院

通讯作者: 柳伟

Abstract

Abstract: The traditional Mean Shift tracking algorithm would lose target when the target is in rapid movement or the background is changing obviously. A Mean Shift tracking algorithm based on background optimization was proposed. The algorithm used cross-join histogram and re-quantilization, and optimized background by decreasing the weight of the Probability Density Function (PDF) of the background pixels. Then the impact of the background on the target region was reduced. The experimental results show that, when the target moves rapidly and the background region changes a lot, the proposed method can still track the target accurately.

Key words: object tracking, Mean Shift algorithm, background optimization

摘要： 针对传统的Mean Shift算法在目标快速运动且背景区域变化较大时，容易丢失跟踪目标的问题，提出了一种基于背景优化的Mean Shift目标跟踪算法。该算法引入混合直方图并对直方图重新量化，再通过减少背景像素在概率密度函数(PDF)中的权重来对背景进行优化，从而降低背景区域对跟踪的影响。实验结果表明，当目标快速运动，且背景区域变化较大时，该算法仍然能够实现对运动目标的准确跟踪。

关键词: 运动目标跟踪, Mean Shift算法, 背景优化

CLC Number:

TP391.41

Wei LIU Yi-ning LUO Nan SUN. Mean Shift tracking algorithm based on background optimization[J]. Journal of Computer Applications.

柳伟罗以宁孙南. 基于背景优化的Mean Shift目标跟踪算法[J]. 计算机应用.

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