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| 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 |
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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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Received: 17 April 2012
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2013, Vol. 26 
