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CLASS: Collaborative Low-Rank and Sparse Separation for Moving Object Detection

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Abstract

Low-rank models have been successfully applied to background modeling and achieved promising results on moving object detection. However, the assumption that moving objects are modelled as sparse outliers limits the performance of these models when the sizes of moving objects are relatively large. Meanwhile, inspired by the visual system of human brain which can cognitively perceive the physical size of the object with different sizes of retina imaging, we propose a novel approach, called Collaborative Low-Rank And Sparse Separation (CLASS), for moving object detection. Given the data matrix that accumulates sequential frames from the input video, CLASS detects the moving objects as sparse outliers against the low-rank structure background while pursuing global appearance consistency for both foreground and background. The sparse and the global appearance consistent constraints are complementary but simultaneously competing, and thus CLASS can detect the moving objects with different sizes effectively. The smoothness constraints of object motion are also introduced in CLASS for further improving the robustness to noises. Moreover, we utilize the edge-preserving filtering method to substantially speed up CLASS without much losing its accuracy. The extensive experiments on both public and newly created video sequences suggest that CLASS achieves superior performance and comparable efficiency against other state-of-the-art approaches.

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  1. Available at: http://chenglongli.cn/people/lcl/journals.html

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Shushan Qu, China

    Aihua Zheng, Minghe Xu, Bin Luo & Chenglong Li

  2. Jiangsu Engineering Centre of Network Monitoring and School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Zhili Zhou

Authors
  1. Aihua Zheng
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  2. Minghe Xu
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  3. Bin Luo
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  4. Zhili Zhou
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  5. Chenglong Li
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Correspondence to Chenglong Li.

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Funding

This study was funded by the National Nature Science Foundation of China (61502006), the Natural Science Foundation of Anhui Province (1508085QF127), and the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2014A015, KJ2015A110, KJ2016A114 and KJ2015ZD44).

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Informed consent was obtained from all individual participants included in the study.

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The authors declare that they have no conflict of interest.

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Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Zheng, A., Xu, M., Luo, B. et al. CLASS: Collaborative Low-Rank and Sparse Separation for Moving Object Detection. Cogn Comput 9, 180–193 (2017). https://doi.org/10.1007/s12559-017-9449-5

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  • DOI: https://doi.org/10.1007/s12559-017-9449-5

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