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Multi-features guided robust visual tracking

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Abstract

This paper focuses on dealing with the tracking challenges such as target occlusion and deformation. It proposes a new tracking method via extracting and evaluating multi-features for both target region and its adjacent surroundings. The multi-features separately describe the key factors to detect target including the color feature, the shape and contour feature, and the distributions of structure and intensity described by the Pearson Correlation Coefficient. These multi-features are proposed as the basic representation of target template and candidates and used to define a matching algorithm between them. The best matched candidate is taken as the final tracking result. To improve the efficiency of target template and candidates, the region of importance (ROI) for target is proposed by evaluating the distribution of salient values on many extended regions. The ROIs produce more accurate regions to form target template and candidates. Finally, a new template update method is defined based on the precision of tracked result to adapt to target state and achieve the follow target tracking. Using 25 videos in visual tracking benchmark, we achieve the quantitative and qualitatively evaluations of 12 different trackers. Many experiments demonstrate that our tracker produces much better results than the present trackers in dealing with target occlusion, deformation, rotation, background clutters.

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Acknowledgments

This paper is funded by some projects of the authors. Yun Liang is the Natural Science Foundation of China (No. 61772209), and the Science and Technology Planning Project of Guangdong Province (No. 2019A050510034, 2019B020219001). Jian Zhang is supported by the Natural Science Foundation of China (No. 61972361). Chen Lin is supported by the Natural Science Foundation of China (No.61472335, 61,972,328). Jun Xiao is supported by the National Natural Science Foundation of China (No.61572431), and the Zhejiang Natural Science Foundation (No.LY17F020009, LR19F020002, LZ17F020001).

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agriculture University, Guangzhou, 510642, China

    Yun Liang & Mei-hua Wang

  2. School of Science and Technology, Zhejiang International Studies University, Hangzhou, 310023, China

    Jian Zhang

  3. Department of Computer Science, Xiamen University, Xiamen, 361005, China

    Chen Lin

  4. College of Computer Science & Technology, Zhejiang University, Hangzhou, 310027, China

    Jun Xiao

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  1. Yun Liang
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  2. Jian Zhang
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  3. Mei-hua Wang
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  4. Chen Lin
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  5. Jun Xiao
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Corresponding author

Correspondence to Jian Zhang.

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CLC number: TP 37

Appendix 1: All The Precision Plots and Success Plots

Appendix 1: All The Precision Plots and Success Plots

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The total precision plots and success plots

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The total precision plots and success plots

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1.1 Appendix 2 The comparisons of total 25 videos

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comparisons of eight videos.

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Comparisons of seven videos

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Comparisons of seven videos.

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Comparisons of three videos

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Liang, Y., Zhang, J., Wang, Mh. et al. Multi-features guided robust visual tracking. Multimed Tools Appl 80, 16367–16395 (2021). https://doi.org/10.1007/s11042-020-08791-z

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  • DOI: https://doi.org/10.1007/s11042-020-08791-z

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