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A novel tracker based on the kernelized correlation filter
Author(s): Dongxun Chen; Zhen Jiang; Yanxia Wei
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Paper Abstract

Correlation filter based tracking methods are the core component of most trackers which achieve the excellent performance in term of the accuracy and robustness in visual tracking. However, there are still lots of challenging situations, such as occlusion or illumination, which confines and limits the performance of trackers. To cope with the above problems, in this paper, we suggest an effective tracking method via part-based strategy. Compared with the conventional tracking algorithms based on correlation filter, our tracker employs the novel strategy to validate and estimate the target’s final position, avoiding merely utilizing the maximum response in the response map as the target position which is often prone to drift away from the target. In addition, to effectively deal with occlusion, we divide the sample into multiple parts. When the sample is partly occluded, the visible part can still provide effective clues for tracking, ensuring the robustness of tracker. A large number of conveys are conducted on the public databases, and experimental results show that the proposed algorithm has obvious performance improvement in the case of dealing with target occlusion, and the real-time performance is also pretty good.

Paper Details

Date Published: 16 October 2019
PDF: 6 pages
Proc. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 112050K (16 October 2019); doi: 10.1117/12.2541636
Show Author Affiliations
Dongxun Chen, Shanghai Univ. (China)
Zhen Jiang, Shanghai Univ. (China)
Yanxia Wei, Shanghai Univ. (China)
Liaocheng Univ. (China)


Published in SPIE Proceedings Vol. 11205:
Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019)
Anand Asundi; Motoharu Fujigaki; Huimin Xie; Qican Zhang; Song Zhang; Jianguo Zhu; Qian Kemao, Editor(s)

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