Salient motion detection using proximal robust principal component analysis method

Pengcheng Wang; Qian Chen; Weixian Qian; Kan Ren; Zheyi Yao; Fuyuan Xu

doi:10.1117/1.JEI.26.2.023004

10 March 2017 Salient motion detection using proximal robust principal component analysis method

Pengcheng Wang, Qian Chen, Weixian Qian, Kan Ren, Zheyi Yao, Fuyuan Xu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 2, 023004 (March 2017). https://doi.org/10.1117/1.JEI.26.2.023004

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 ℓ1 -penalized optimization. Real data experiments reveal that even if ℓ1-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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Pengcheng Wang, Qian Chen, Weixian Qian, Kan Ren, Zheyi Yao, and Fuyuan Xu "Salient motion detection using proximal robust principal component analysis method," Journal of Electronic Imaging 26(2), 023004 (10 March 2017). https://doi.org/10.1117/1.JEI.26.2.023004

Received: 9 September 2016; Accepted: 19 December 2016; Published: 10 March 2017

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