On particle filter and Mean Shift tracking algorithm based on multi-feature fusion

Author(s):  
Nan Qiao ◽  
Jin-xia Yu
2010 ◽  
Vol 30 (3) ◽  
pp. 643-645 ◽  
Author(s):  
Wei ZENG ◽  
Gui-bin ZHU ◽  
Jie CHEN ◽  
Ding-ding TANG

2010 ◽  
Vol 32 (2) ◽  
pp. 411-415 ◽  
Author(s):  
Yuan-zheng Li ◽  
Zhao-yang Lu ◽  
Quan-xue Gao ◽  
Jing Li

2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.


2014 ◽  
Vol 701-702 ◽  
pp. 257-260
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang

In order to improve the robustness of visual tracking in complex environments, a novel multi-feature fusion tracking method based on mean shift and particle filter is proposed. In the proposed method, the color and shape information are adaptively fused to represent the target observation, and incorporating mean shift method into particle filter method. The method can overcome the degeneracy problem of particle. Experimental results demonstrate that this method can improve stability and accuracy of tracking.


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