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Salient motion detection using proximal robust principal component analysis method

[+] Author Affiliations
Pengcheng Wang, Kan Ren

Nanjing University of Science and Technology, Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing 210094, China

Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology, Beijing 100876, China

Qian Chen, Weixian Qian, Zheyi Yao

Nanjing University of Science and Technology, Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing 210094, China

Fuyuan Xu

China Aerospace Science and Industry Corpora, 8511 Research Institute, Nanjing 210002, China

J. Electron. Imaging. 26(2), 023004 (Mar 10, 2017). doi:10.1117/1.JEI.26.2.023004
History: Received September 9, 2016; Accepted December 19, 2016
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Abstract.  The recently proposed robust principal component analysis (RPCA) theory and its derived methods have attracted much attention in many computer vision and machine intelligence applications. From a wide view of these methods, independent motion objects are modeled as pixel-wised sparse or structurally sparse outliers from a highly correlated background signal, and all these methods are implemented under an 1 -penalized optimization. Real data experiments reveal that even if 1-penalty is convex, the optimization sometimes cannot be satisfactorily solved, especially when the signal-to-noise ratio is relatively high. In addition, the unexpected background motion (e.g., periodic or stochastic motion) may also be included. We propose a moving object detection method based on a proximal RPCA along with saliency detection. Convex penalties including low-rank and sparse regularizations are substituted with proximal norms to achieve robust regression. After the foreground candidates have been extracted, a motion saliency map using spatiotemporal filtering is constructed. The foreground objects are then filtered out by dynamically adjusting the penalty parameter according to the corresponding saliency values. Evaluations on challenging video clips and qualitative and quantitative comparisons with several state-of-the-art methods demonstrate that the proposed approach works efficiently and robustly.

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Citation

Pengcheng Wang ; Qian Chen ; Weixian Qian ; Kan Ren ; Zheyi Yao, et al.
"Salient motion detection using proximal robust principal component analysis method", J. Electron. Imaging. 26(2), 023004 (Mar 10, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023004


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