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Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization

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Abstract

Moving object detection is one of the significant tasks in computer vision. Robust Principal Component Analysis (RPCA) is a common method for moving object detection. However, this algorithm fails to effectively utilize the low-rank prior information of the background and the spatio-temporal continuity of the foreground, and exhibits degraded performance in the existence of shadows, photometric variations, rapidly moving objects, dynamic backgrounds, and jitters. Therefore, a new model via RPCA framework using non-convex low-rank approximation and total variational regularization is proposed to detect moving objects. Firstly, this paper introduces a non-convex function to deal with the problem that the nuclear norm excessively penalizes large singular values for the sake of constraining the low-rank characteristics of video background availably. Then, the sparsity is strengthened with the l1-norm and the spatio-temporal continuity of the foreground is explored by TV regularization. Finally, the augmented Lagrange multiplier algorithm is expanded by the alternating direction multiplier strategy to solve the model. Extensive experiments show that the proposed method outperforms existing methods in terms of the accuracy of moving object detection and foreground extraction effect.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (Nos. 62173127, 61803146 and 61973104), the Scientific and Technological Innovation Leaders in Central Plains (No. 224200510008), the Henan Excellent Young Scientists Fund (No. 212300410036), the Program for Science and Technology Innovation Talents in Universities of Henan Province under Grant (No. 21HASTIT029), the Training Program for Young Backbone Teachers in Universities of Henan Province under Grant (No. 2019GGJS089), the Key Science and Technology Projects in Henan Province under Grant (Nos. 212102210169 and 212102210086), the Innovative Funds Plans of Henan University of Technology under Grant (No. 2020ZKCJ06), the Zhengzhou Science and Technology Collaborative Innovation Project (No. 21ZZXTCX06), the Cultivation Program of Young Backbone Teachers in Henan University of Technology under Grant chentianfei, the Open Fund from Research Platform of Grain Information Processing Center in Henan University of Technology under Grant (Nos. KFJJ-2020-107, KFJJ-2020-111 and KFJJ-2020-114).

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Authors and Affiliations

  1. Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou, 450001, China

    Tianfei Chen, Dongliang Zhao, Lijun Sun, Shi Li & Binbin Feng

  2. Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou, 450001, China

    Tianfei Chen, Dongliang Zhao, Lijun Sun, Shi Li & Binbin Feng

  3. College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China

    Tianfei Chen, Dongliang Zhao, Lijun Sun, Shi Li & Binbin Feng

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  1. Tianfei Chen
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  2. Dongliang Zhao
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  4. Shi Li
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Correspondence to Lijun Sun.

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Chen, T., Zhao, D., Sun, L. et al. Moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization. SIViP 17, 109–117 (2023). https://doi.org/10.1007/s11760-022-02210-6

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