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Patch-based visual tracking with online representative sample selection

[+] Author Affiliations
Weihua Ou, Bin Liu, Daoxun Xia

Guizhou Normal University, School of Big Data and Computer Science, Guiyang, China

Di Yuan, Donghao Li

Harbin Institute of Technology Shenzhen Graduate School, School of Computer Science, Shenzhen, China

Wu Zeng

Wuhan Polytechnic University, School of Electric and Electronic Engineering, Wuhan, China

J. Electron. Imaging. 26(3), 033006 (May 13, 2017). doi:10.1117/1.JEI.26.3.033006
History: Received December 22, 2016; Accepted April 25, 2017
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Abstract.  Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

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© 2017 SPIE and IS&T

Citation

Weihua Ou ; Di Yuan ; Donghao Li ; Bin Liu ; Daoxun Xia, et al.
"Patch-based visual tracking with online representative sample selection", J. Electron. Imaging. 26(3), 033006 (May 13, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033006


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