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Journal of Electronic Imaging

Patch-based visual tracking with online representative sample selection
Author(s): Weihua Ou; Di Yuan; Donghao Li; Bin Liu; Daoxun Xia; Wu Zeng
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Paper 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.

Paper Details

Date Published: 13 May 2017
PDF: 12 pages
J. Electron. Imag. 26(3) 033006 doi: 10.1117/1.JEI.26.3.033006
Published in: Journal of Electronic Imaging Volume 26, Issue 3
Show Author Affiliations
Weihua Ou, Guizhou Normal Univ. (China)
Di Yuan, Harbin Institute of Technology Shenzhen Graduate School (China)
Donghao Li, Harbin Institute of Technology Shenzhen Graduate School (China)
Bin Liu, Guizhou Normal Univ. (China)
Daoxun Xia, Guizhou Normal Univ. (China)
Wu Zeng, Wuhan Polytechnic Univ. (China)

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