Abstract
Traditional robust principal component analysis (RPCA) is very prone to voids in the process of background/foreground separation of complex scene videos and easy to misjudge the dynamic background as a moving target, which makes the separation effect unideal. In order to address this problem, this article introduces the super-pixel segmentation technique into the RPCA model. First, the Linear Spectral Clustering algorithm (LSC) is used to mark the super-pixel segmentation of the video sequence and a super-pixel grouping matrix is obtained. Then a new video background/foreground separation model is proposed based on the non-convex rank approximation RPCA and super-pixel motion detection (SPMD) technique. The Otsu algorithm is used to obtain the motion mask matrix and the augmented lagrange alternating direction method is used to solve the improved RPCA model. The results of numerical experiment show that the method proposed in this article has a higher accuracy in the detection of moving objects in dynamic background.
Highlights
V IDEO background/foreground separation [1]–[3] is a key pre-processing step in the surveillance system
Considering the success of super-pixel segmentation technique in image processing and the advantages of nonconvex-rank-approximation-based robust principal component analysis (RPCA) model in video foreground/ background separation and moving target detection under dynamic background, this paper proposed a new video pre-background separation model (LSCNCRPCA) based on the non-convex rank approximation RPCA and super-pixel motion detection technique
In order to overcome the problem of large computational complexity for solving the nuclear norm, scholars proposed RPCA models based on low-rank matrix decomposition technique [32], [33], which decomposes the low-rank matrix into two or three smaller matrix, reducing the size of the matrix that requires singular value decomposition and the amount of calculation as well
Summary
V IDEO background/foreground separation [1]–[3] is a key pre-processing step in the surveillance system It has a wide range of applications in the fields of intelligent traffic management, intelligent video surveillance and sports behavior analysis. The RPCA model achieves the detection of moving targets by decomposing the video matrix into a low-rank background matrix and a sparse moving foreground target matrix. This kind of method can estimate the background and separate the foreground moving target at the same time, effectively overcome the target false detection.
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