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Anti-occlusion object tracking based on correlation filter

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Abstract

Despite remarkable progress, visual object tracking is still a challenging task as objects usually suffer from significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an anti-occlusion correlation filter-based tracking method (AO-CF) for robust visual tracking. We first propose an occlusion criterion based on continuous response values. Based on the criterion, objects are divided into four categories to adaptively identify the occlusion of objects. Then we propose a new detection condition for detecting proposals. When the occlusion criterion is triggered, the re-detection mechanism is executed and the tracker is commanded to stop, and then the re-detector selects the most reliable proposal to reinitialize the tracker. Experimental results show that our method outperforms other state-of-the-art trackers in terms of both precision rate and success rate on the widely used object tracking benchmark dataset. In addition, AO-CF is able to achieve real-time tracking speed.

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Acknowledgements

This paper is sponsored by National Program on Key Basic Research Project (2014CB744903), National Natural Science Foundation of China (61973212, 61673270), Shanghai Science and Technology Committee Research Project (17DZ1204304).

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jun Liu, Gang Xiao, Xingchen Zhang, Ping Ye & Shengyun Peng

  2. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China

    Jun Liu & Xingzhong Xiong

  3. College of Civil Engineering, Tongji University, Shanghai, 20092, China

    Shengyun Peng

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  1. Jun Liu
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  2. Gang Xiao
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  3. Xingchen Zhang
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Correspondence to Xingchen Zhang.

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Liu, J., Xiao, G., Zhang, X. et al. Anti-occlusion object tracking based on correlation filter. SIViP 14, 753–761 (2020). https://doi.org/10.1007/s11760-019-01601-6

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