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A fast method for moving object detection in video surveillance image

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Abstract

Moving object detection and extraction are widely used in video surveillance and image processing. In this paper, we present a fast method for moving object detection. We use weights of the Gaussian distribution as decision factors, update parameters of the Gaussian mixture model if its values are smaller than that of those not belonging to the background; otherwise, no updates are done. It improves the existing methods by updating the Gaussian mixture model selectively. Experimental results on various scenes of video surveillance show that computation time of the proposed method is reduced.

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Acknowledgements

The work of this paper is supported by National Natural Science Foundation of China (No. 51375132), Jincheng Science and Technology Foundation, China (No. 201501004-5), Shanxi Provincial Natural Science Foundation, China (No. 2013011017) and Ph.D. Foundation of Taiyuan University of Science and Technology, China (No. 20122025). We also thank Chen’s work for this paper.

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Rongguo Zhang & Jing Hu

  2. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China

    Xiaojun Liu & Kun Liu

  3. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849-3547, USA

    Kai Chang

Authors
  1. Rongguo Zhang
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  2. Xiaojun Liu
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  3. Jing Hu
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  4. Kai Chang
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  5. Kun Liu
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Correspondence to Rongguo Zhang.

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Zhang, R., Liu, X., Hu, J. et al. A fast method for moving object detection in video surveillance image. SIViP 11, 841–848 (2017). https://doi.org/10.1007/s11760-016-1030-2

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  • DOI: https://doi.org/10.1007/s11760-016-1030-2

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