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An improved total variation regularized RPCA for moving object detection with dynamic background

  • * Corresponding author: Ying Yang

    * Corresponding author: Ying Yang 

This work was supported in part by the National Natural Science Foundation of China (61633001, 11671029, 11601348) and the 111 Project of China (B16002)

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  • Dynamic background extraction has been a fundamental research topic in video analysis. In this paper, a novel robust principal component analysis (RPCA) approach for foreground extraction is proposed by decomposing video frames into three parts: rank-one static background, dynamic background and sparse foreground. First, the static background is represented by a rank-one matrix, which can avoid the computation of singular value decomposition because usually the dimensionality of a surveillance video is very large. Secondly, the dynamic background is characterized by $ \ell_{2,1} $-norm, which can exploit the shared information. Thirdly, the sparse foreground is enhanced by total variation, which can preserve sharp edges that are usually the most important for clear object extraction. An efficient symmetric Gauss-Seidel based alternating direction method of multipliers (sGS-ADMM) is established with convergence analysis. Extensive experiments on real-world datasets show that our proposed approach outperforms the existing state-of-the-art approaches. In fact, to the best of our knowledge, this is the first time to integrate the joint sparsity and total variation into a RPCA framework, which has demonstrated the superiority of performance.

    Mathematics Subject Classification: Primary: 90C25, 90C90; Secondary: 65K10.


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  • Figure 1.  Illustrations of the importance associated with the joint sparsity. For the left side, the first row is the foreground frames, the second and third row are the corresponding dynamic background frames and dynamic foreground frames, respectively. For the right side, the column represents the frames of the dynamic background, and it is not hard to conclude that this matrix has dense elements row-wise and sparse elements column-wise

    Figure 2.  Some frames of tested surveillance videos. (a) The left two columns: $ noncamouflage $ and $ noisynight $. (b) The middle two columns: $ snowfall $ and $ skating $. (c) The right two columns: $ fall $ and $ fountain $

    Figure 3.  Visual Comparisons on both Synthetic and Real-world Data Sets

    Table 1.  Quantitative Results on both Synthetic and Real-world Data Sets

    Videos RPCA Ours Ours
    $ \; $ R P F R P F R P F
    noncamouflage 0.64 0.35 0.45 0.65 0.36 0.47 0.69 0.78 0.73
    noisynight 0.50 0.20 0.29 0.40 0.19 0.26 0.45 0.67 0.54
    snowfall 0.74 0.37 0.49 0.75 0.41 0.53 0.87 0.92 0.89
    skating 0.72 0.51 0.60 0.75 0.66 0.70 0.74 0.86 0.80
    fall 0.68 0.13 0.21 0.69 0.13 0.21 0.83 0.94 0.88
    fountain 0.84 0.02 0.04 0.74 0.03 0.06 0.74 0.08 0.14
     | Show Table
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