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A universal sample-based background subtraction method for traffic surveillance videos

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Abstract

Background subtraction in traffic surveillance videos plays a crucial role in many high-level analytics and applications. Although great progress has been made in background subtraction, there are still many challenges in real traffic surveillance circumstances, such as camouflaged vehicles and dynamic background. This paper proposes a universal and accurate sampled-based vehicle detection method. The proposed method uses the color and Haar features to construct the background model, which can increase the accuracy of vehicle detection for camouflaged vehicles and is robust to low visibility. Besides, to reduce the incorrect samples of the initialized background model from a single image, the samples of the initial background model are randomly chosen from a similar candidate set. Furthermore, a novel random strategy is proposed to update the background pixel itself, while a combination update strategy with certainty and randomness is adopted for its neighborhood. This updating mechanism speeds up the suppression of ghosts in the background model. Experimental results verify the excellent behavior of our proposed method when compared to other mainstream methods.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities under grant NS2018044. The authors would like to thank the associate editor and anonymous reviewers for their constructive and valuable comments and thank P. St-Charles and O. Barnish for publishing their codes on the internet.

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Weili Zeng & Zhao Yang

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

    Chao Xie & Xiaobo Lu

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  1. Weili Zeng
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  2. Chao Xie
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  3. Zhao Yang
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  4. Xiaobo Lu
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Correspondence to Weili Zeng.

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Zeng, W., Xie, C., Yang, Z. et al. A universal sample-based background subtraction method for traffic surveillance videos. Multimed Tools Appl 79, 22211–22234 (2020). https://doi.org/10.1007/s11042-020-08948-w

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  • DOI: https://doi.org/10.1007/s11042-020-08948-w

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