计算机工程Issue(11)：14-17,4.DOI:10.3969/j.issn.1000-3428.2014.11.003
基于多特征融合与均值偏移的粒子滤波跟踪算法
Particle Filtering Tracking Algorithm Based on Multi-feature Fusion and Mean Shift
摘要
Abstract
To solve the problem that a single feature leads to tracking failure easily in a complex environment,a Particle Filtering( PF) tracking algorithm based on multi-feature fusion and Mean Shift( MS) is proposed. Under the framework of PF,it is closer to the real posterior distribution by embedding MS algorithm and using color and structural as the observation model to represent the object,and the weights of particles are calculated by this integration,in order to reduce the tracking deviation. Experimental results show that the proposed algorithm has better robustness when using the same particles,and the average weight of the particle is improved and the resample times are reduced significantly,even using the less particles can achieve tracking stability.关键词
目标跟踪/均值偏移/多特征融合/粒子滤波/颜色特征/结构特征Key words
object tracking/Mean Shift ( MS )/multi-feature fusion/Particle Filtering ( PF )/color feature/structural feature分类
信息技术与安全科学引用本文复制引用
于金霞,乔楠..基于多特征融合与均值偏移的粒子滤波跟踪算法[J].计算机工程,2014,(11):14-17,4.基金项目
河南省重点科技攻关计划基金资助项目(122102310309) （122102310309）
河南省基础与前沿技术研究基金资助项目(142300410147) （142300410147）
河南理工大学博士基金资助项目(B2011-58)。 （B2011-58）
