Particle Filter and Mean Shift Tracking Method Based on Multi-feature Fusion

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


2013 ◽  
Vol 846-847 ◽  
pp. 1217-1220
Author(s):  
Yuan Zheng Li

Traditional tracking algorithm is not compatible between robustness and efficiency, under complex scenes, the stable template update strategy is not robust to target appearance changes. Therefore, the paper presents a dynamic template-update method that combined with a mean-shift guided particle filter tracking method. By incorporating the original information into the updated template, or according to the variety of each component in template to adjust the updating weights adaptively, the presented algorithm has the natural ability of anti-drift. Besides, the proposed method cope the one-step iteration of mean-shift algorithm with the particle filter, thus boost the performance of efficiency. Experimental results show the feasibility of the proposed algorithm in this paper.


2013 ◽  
Author(s):  
La Zhang ◽  
Yingyun Yang ◽  
Huabing Wang ◽  
Yansi Yang ◽  
Bo Liu

2018 ◽  
Vol 12 (9) ◽  
pp. 1529-1540 ◽  
Author(s):  
Ruohong Huan ◽  
Shenglin Bao ◽  
Chu Wang ◽  
Yun Pan

2012 ◽  
Vol 78 (787) ◽  
pp. 799-811 ◽  
Author(s):  
Kazuki TADA ◽  
Ming DING ◽  
Hiroshi TAKEMURA ◽  
Hiroshi MIZOGUCHI

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