An Online Learning Visual Tracking Method Based On Compressive Sensing

2015 ◽  
Vol 35 (9) ◽  
pp. 0915001 ◽  
Author(s):  
刘威 Liu Wei ◽  
赵文杰 Zhao Wenjie ◽  
李成 Li Cheng
IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14790-14798 ◽  
Author(s):  
Weiming Yang ◽  
Meirong Zhao ◽  
Yinguo Huang ◽  
Yelong Zheng

2016 ◽  
Vol 177 ◽  
pp. 612-619 ◽  
Author(s):  
Ming-Liang Gao ◽  
Jin Shen ◽  
Li-Ju Yin ◽  
Wei Liu ◽  
Guo-Feng Zou ◽  
...  

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2382 ◽  
Author(s):  
Guokai Shi ◽  
Tingfa Xu ◽  
Jiqiang Luo ◽  
Jie Guo ◽  
Zishu Zhao

2013 ◽  
Vol 457-458 ◽  
pp. 1028-1031
Author(s):  
Ying Hong Xie ◽  
Cheng Dong Wu

Considering the process of objects imaging in the camera is essentially the projection transformation process. The paper proposes a novel visual tracking method using particle filtering on SL(3) group to predict the changes of the target area boundaries of next moment, which is used for dynamic model. Meanwhile, covariance matrices are applied for observation model. Extensive experiments prove that the proposed method can realize stable and accurate tracking for object with significant geometric deformation, even for nonrigid objects.


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