Background recovery from video sequences via online motion-assisted RPCA
Jiaoru Yang, Jingyu Yang, Xuemeng Yang, Huanjing Yue · 2016
Background modeling is an important technique for video analysis. Robust principal component analysis (RPCA) assisted with motion information has shown improved background recovery performance, but still suffers from the deficiency in handling steaming video due to the batch-mode formulation and implementation. This paper proposes an online motion-assisted robust principal component analysis (OMA-RPCA) model for background recovery from video sequences. The inherent batch-mode nuclear norm for low-rank approximation is replaced with an explicitly low-rank matrix factorization. Motion information extracted by an optical flow method is incorporated into the data term to facilitate the separation of moving objects from the background. The proposed model is effectively solved by an alternating optimization scheme in an online mode. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods with lower memory cost and scalability to online applications.