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Robust visual tracking using self-adaptive strategy

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Abstract

Discriminative correlation filter-based algorithms have recently demonstrated prominent advantages in the community of computer visual tracking, due to their ability to convert ridge regression problems in the frequency domain for creating solutions efficiently, which has attracted a great deal of attention and spurred new research. High precision and robustness have always been the goals of visual tracking. However, during the tracking process, target objects often encounter sophisticated scenarios such as fast motion and occlusion. During this period, erroneous tracking information will be generated and delivered to the next frame for updating; the information will seriously deteriorate the overall tracking model. To address the problem mentioned above, in this paper, we propose an accurate model self-adaptive update method based on a discriminative correlation filter framework. The proposed tracking method is achieved by utilizing the peak score of a response map generated by the discriminative correlation filter as a dynamic threshold with comparisons to its PSR (peak side-lobe ratio) scores, and then the comparative results are used as the differentiated condition for updating the translation filter and scale filter model. In addition, multiple hand-crafted features such as HOG (histogram of oriented gradient), CN (color names), and HOI (histogram of local intensities) are fused self-adaptively for comprehensive feature representation, which further improve tracking performance. We evaluate the performance of the proposed tracker on OTB benchmark datasets; the experimental results demonstrate that the proposed tracker performs favorably against most state-of-the-art discriminative correlation filter-based trackers including some methods follow deep learning paradigm, and the effectiveness of updating the model self-adaptive is verified.

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Acknowledgments

This work was supported by the grants from National Natural Science Foundation, China under Grant 61605048, and Grant 61603144, and Grant 61403245 and Grant 91648119, in part by Natural Science Foundation of Fujian Province, China under Grant 2015 J01256, and Grant 2016 J01300, in part by the Talent project of Huaqiao University under Grant 14BS215, in part by Quanzhou scientific and technological planning projects of Fujian, China under Grant 2015Z120 and Grant 2017G024, and in part by the Subsidized Project for Postgraduates ‘Innovative Fund in Scientific Research of Huaqiao University under Grant 1611422001.

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Authors and Affiliations

  1. College of Engineering, Huaqiao University, Quanzhou, Fujian, China

    Zhi Chen, Peizhong Liu & Yongzhao Du

  2. Research Center of Applied Statistics and Big Data, Huaqiao University, No. 668, Jimei Avenue, Xiamen, 361021, China

    Peizhong Liu & Yongzhao Du

  3. College of computer science and technology, Huaqiao University, Xiamen, Fujian, China

    Yanmin Luo

  4. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Jing-Ming Guo

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  1. Zhi Chen
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  2. Peizhong Liu
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  3. Yongzhao Du
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Correspondence to Peizhong Liu.

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Chen, Z., Liu, P., Du, Y. et al. Robust visual tracking using self-adaptive strategy. Multimed Tools Appl 79, 141–162 (2020). https://doi.org/10.1007/s11042-019-08069-z

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