Abstract
Robust Principal Component Analysis (RPCA) has been proved to be effective for the moving object detection with background variation. Alternating Direction Method (ADM) based RPCA takes full advantages of the separable structure of the objective function to achieve better results than traditional RPCA methods. But it suffers from the heavy computing burden and low efficiency. In this paper, a Symmetric Alternating Direction Method (SADM) is proposed to solve above problems. SADM optimizes the iterative strategy of ADM by updating the multiplier of the linear constraint twice every iteration which speeds up the convergence, thus reduces the execution times of Singular Value Decomposition (SVD). Besides, the new equilibrium parameter and interrupt mechanism are introduced to guarantee the object detection accuracy and avoid the unnecessary iterations. Compared with ADM, the experimental results show that not only the detection accuracy of proposed method is improved by 46.8%, but also the time consumption is reduced by 97.5%.
This work was supported by the Project of Beijing Municipal Commission of Education (KM201710028017), the National Natural Science Foundation of China (61572331, 61472468, 61373034), the National Key Technology Research and Development Program (2015BAF13B01), the International Cooperation Program on Science and Technology (2011DFG13000), the Project of Beijing Municipal Science & Technology Commission (Z141100002014001), the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (IDHT20150507), and Training young backbone talents personal projects (2014000020124G135).
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Shao, Z., Wu, G., Qu, Y., Shi, Z., Guan, Y., Tan, J. (2018). Robust Principal Component Analysis via Symmetric Alternating Direction for Moving Object Detection. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_27
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