Improved object tracking method based on mean shift and particle filter

2013 ◽  
Vol 32 (2) ◽  
pp. 504-506
Author(s):  
Ke LI ◽  
Ke-hu XU ◽  
Da-shan HUANG
2010 ◽  
Vol 32 (2) ◽  
pp. 411-415 ◽  
Author(s):  
Yuan-zheng Li ◽  
Zhao-yang Lu ◽  
Quan-xue Gao ◽  
Jing Li

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.


2010 ◽  
Vol 44-47 ◽  
pp. 3902-3906
Author(s):  
Jie Jia ◽  
Yong Jun Yang ◽  
Yi Ming Hou ◽  
Xiang Yang Zhang ◽  
He Huang

An object tracking framework based on adaboost and Mean-Shift for image sequence was proposed in the manuscript. The object rectangle and scene rectangle in the initial image of the sequence were drawn and then, labeled the pixel data in the two rectangles with 1 and 0. Trained the adaboost classifier by the pixel data and the corresponding labels. The obtained classifier was improved to be a 5 class classifier and employed to classify the data in the same scene region of next image. The confidence map including 5 values was got. The Mean-Shift algorithm is performed in the confidence map area to get the final object position. The rectangles of object and background were moved to the new position. The object rectangle was zoomed by 5 percent to adapt the object scale changing. The process including drawing rectangle, training, classification, orientation and zooming would be repeated until the end of the image sequence. The experiments result showed that the proposed algorithm is efficient for nonrigid object orientation in the dynamic scene.


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

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