吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (02): 407-411.

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Adaptive tracking algorithm based on particle filter-mean shift

LI Wen-hui, ZHOU Qiang, WANG Ying, ZHANG De-cai   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2011-03-19 Online:2012-03-01 Published:2012-03-01

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Abstract: An adaptive tracking algorithm based on the combination of particle filter and mean shift is proposed. First, the tracking target is segmented into different block templates, and the block template with the least color similarity to the surroundings is chosen as a tracking region. Then, the block template is adjusted adaptively to the best scale when it is tracked by the proposed particle filter method based on the mean shift. Finally, an adaptive observation model is introduced after the state estimation of particle filter, and the Gaussian noise variance and the number of particles are determined according to the change of the block template scale. Experimental results show that the performance of the proposed algorithm is better than traditional method when target body is partially occluded or its moving direction is changed.

Key words: computer application, adaptive tracking, meanshift-particle filter, Gaussian noise variance

CLC Number: 

  • TP391
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