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Proceedings Paper

Discriminative correlation filter tracking with occlusion detection
Author(s): Shuo Zhang; Zhong Chen; XiPeng Yu; Ting Zhang; Jing He
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Paper Abstract

Aiming at the problem that the correlation filter-based tracking algorithm can not track the target of severe occlusion, a target re-detection mechanism is proposed. First of all, based on the ECO, we propose the multi-peak detection model and the response value to distinguish the occlusion and deformation in the target tracking, which improve the success rate of tracking. And then we add the confidence model to update the mechanism to effectively prevent the model offset problem which due to similar targets or background during the tracking process. Finally, the redetection mechanism of the target is added, and the relocation is performed after the target is lost, which increases the accuracy of the target positioning. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art methods in terms of robustness and accuracy.

Paper Details

Date Published: 8 March 2018
PDF: 5 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090X (8 March 2018); doi: 10.1117/12.2284989
Show Author Affiliations
Shuo Zhang, Huazhong Univ. of Science and Technology (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
XiPeng Yu, Huazhong Univ. of Science and Technology (China)
Ting Zhang, Huazhong Univ. of Science and Technology (China)
Jing He, Henan Univ. (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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