CS-RPCA: CLUSTERED SPARSE RPCA FOR MOVING OBJECT DETECTION

Moving object detection (MOD) is an important step for many computer vision applications. In the last decade, it is evident that RPCA has shown to be a potential solution for MOD and achieved a promising performance under various challenging background scenes. However, because of the lack of different types of features, RPCA still shows degraded performance in many complicated background scenes such as dynamic backgrounds, cluttered foreground objects, and camouflage. To address these problems, this paper presents a Clustered Sparse RPCA (CS-RPCA) for MOD under challenging environments. The proposed algorithm extracts multiple features from video sequences and then employs RPCA to get the low-rank and sparse component from each representation. The sparse subspaces are then emerged into a common sparse component using Grassmann manifold. We proposed a novel objective function which computes the composite sparse component from multiple representations and it is solved using non-negative matrix factorization method. The proposed algorithm is evaluated on two challenging datasets for MOD. Results demonstrate excellent performance of the proposed algorithm as compared to existing state-of-the-art methods.