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A robust tracker integrating particle filter into correlation filter framework

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Abstract

The location and scale filters in discriminative correlation filter methods are lack of accurate rotation representation capability and updated with fixed intervals, which leads to tracking failure and time-consuming in complex scenarios. In this manuscript, a robust tracker integrating particle filter into correlation filter is presented to cope with sharp rotation and remarkable deformation. The target position and scale factor are firstly estimated from the correlation filter, and then the rotation factor is determined by similarity between candidates and template based on the particle filter. As a result, target variation can be accurately described with position, scale and rotation factor. Moreover, a long-time and short-time update scheme is proposed to solve target template drifting problem. Extensive experimental results conducted on OTB-2013, OTB-2015 and VOT-2016 show that the proposed tracker improves the accuracy and robustness of discriminative correlation filter methods.

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Acknowledgments

The authors would like to thank the editors and the anonymous reviewers, whose comments helped to improve the paper greatly. This work was supported by the National Natural Science Foundation of China (No. 61461028) and the National Natural Science Foundation of China (No. 61861027).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou City, China

    Weirong Liu, Huiling Gao, Binshan Li & Xuhui Song

  2. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou City, China

    Jie Liu

  3. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou City, China

    Chaorong Liu

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  1. Weirong Liu
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Correspondence to Weirong Liu.

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Liu, W., Gao, H., Liu, J. et al. A robust tracker integrating particle filter into correlation filter framework. Multimed Tools Appl 79, 28431–28452 (2020). https://doi.org/10.1007/s11042-020-09240-7

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