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Robust object tracking using a sparse coadjutant observation model

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Abstract

This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task reverse sparse representation to get a DSS map in the DSS tracker, we introduce a sparse generative model (SGM) to handle the appearance variation in the DSS tracker. With the alliance of the DSS map and the SGM, our proposed method can track the object under the occlusion and appearance variations effectively. Numerous experiments on various challenging videos of a tracking benchmark illustrate that the proposed tracker performs favorably against several state-of-the-art trackers.

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Acknowledgments

This work was funded by the National Natural Science Foundation of China (61571410 and 61672477) and the Zhejiang Provincial Nature Science Foundation of China (LY18F020018).

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Authors and Affiliations

  1. Department of Information Sciences and Mathematics, China Jiliang University, Hangzhou, 310018, Zhejiang Province, People’s Republic of China

    Jianwei Zhao, Weidong Zhang & Feilong Cao

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  1. Jianwei Zhao
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  2. Weidong Zhang
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  3. Feilong Cao
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Correspondence to Feilong Cao.

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Zhao, J., Zhang, W. & Cao, F. Robust object tracking using a sparse coadjutant observation model. Multimed Tools Appl 77, 30969–30991 (2018). https://doi.org/10.1007/s11042-018-6132-0

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  • DOI: https://doi.org/10.1007/s11042-018-6132-0

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