Mixture Particle PHD Filter Based Multi-Target Visual Tracking
LIN Qing1,2,XU Xiao-Gang1,ZHAN Yong-Zhao1,LIAO Ding-An1,3,YANG Ya-Ping1
1.School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013 2.School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 3.Changzhou Textile Garment Institute,Changzhou 213164
Abstract：Aiming at the problem that particle filter is poor at consistently maintaining the multi-modality of the target distributions for multi-targets in a variable number of visual tracking,a multi-target visual tracking approach based on mixture particle probability hypothesis density (PHD) filter is proposed. The particles are clustered by the K-means algorithm,the classified particles are labeled and the particle filters are separately used for each classified particles. It improves the accuracy of target states estimation and effectively maintains the multi-modal distribution of the various objectives. The experimental results show that the proposed approach is an effective solution to the appearance,merger,separation and other multi-target tracking problems for the new target.
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2013, Vol. 26 
