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Robust visual tracking using discriminative sparse collaborative map

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Abstract

Visual tracking is a challenging task as it needs to consider the appearance variations due to some intrinsic and extrinsic interference factors in the process of tracking. This paper proposes a robust visual tracking algorithm based on the discriminative sparse collaborative (DSC) map and the alternating direction method of multipliers (ADMM). In the proposed visual tracker, named DSC tracker, a novel multi-task reverse sparse representation model based on the group sparse representation and group collaborative representation is proposed. Different from the traditional trackers that use the accelerated proximal gradient for solution, an effective method called ADMM is adopted to solve the proposed optimization model. With the solution, we can construct the discriminative features that contain the sparsity and coordination for the candidates on all templates simultaneously. Many comparison experiments illustrate that the proposed DSC tracker outperforms the DSS tracker as well as several state-of-the-art trackers.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (61571410) and the Zhejiang Provincial Nature Science Foundation of China (LY18F020018 and LSY19F020001).

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Authors and Affiliations

  1. Department of Information Sciences and Mathematics, China Jiliang University, Hangzhou, 310018, China

    Zhenghua Zhou, Weidong Zhang & Jianwei Zhao

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Jianwei Zhao

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  1. Zhenghua Zhou
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  2. Weidong Zhang
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  3. Jianwei Zhao
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Correspondence to Jianwei Zhao.

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Zhou, Z., Zhang, W. & Zhao, J. Robust visual tracking using discriminative sparse collaborative map. Int. J. Mach. Learn. & Cyber. 10, 3201–3212 (2019). https://doi.org/10.1007/s13042-019-01011-7

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