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SCRM: self-correlated representation model for visual tracking

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Abstract

Sparse representation (SR) as a seminal model for visual tracking explores the relationship between all candidates and the observed templates. Different from SR-based trackers, we propose a self-correlated representation model for robust visual tracking. Firstly, we learn a low-dimensional subspace representation from highly correlated templates to model the object, which aims at eliminating the redundant information and reducing the influence of noisy templates. Then, we represent the subspace by itself to learn the inner underlying features from subspace vectors. To further enhance model’s discriminating power, a new observation model is developed by considering both error distribution and large outliers. Experiments are conducted on some challenging video clips and demonstrate the favorable performance of our tracking system compared to some state-of-the-art representation-based trackers.

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Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and constructive suggestions. This work is supported by the Scientific Research Foundation of Graduate School of Southeast University (No. YBJJ1768), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX17_0101), the National Natural Science Foundation of China (No. 61871123), Key Research and Development Program in Jiangsu Province (No. BE2016739) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Shengqin Jiang & Xiaobo Lu

  2. College of Mechanical and Electronical Engineering, Nanjing Forestry University, Nanjing, 210037, People’s Republic of China

    Fengna Cheng

  3. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, People’s Republic of China

    Shengqin Jiang & Xiaobo Lu

Authors
  1. Shengqin Jiang
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  2. Xiaobo Lu
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  3. Fengna Cheng
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Correspondence to Xiaobo Lu.

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Jiang, S., Lu, X. & Cheng, F. SCRM: self-correlated representation model for visual tracking. Soft Comput 24, 2187–2199 (2020). https://doi.org/10.1007/s00500-019-04052-w

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