Improving OR-PCA via smoothed spatially-consistent low-rank modeling for background subtraction

Abstract

Background subtraction is a powerful mechanism for moving object detection. In addition to the most popular dynamic background scenes and abrupt lighting condition limitations for designing robust background subtraction mechanism, jitter-induced motion also poses a great challenge. In this case background subtraction becomes more challenging. Although, robust principal component analysis (RPCA) provides a potential solution for moving object detection but many existing RPCA methods for background subtraction still produce abundant false positives in the presence of these challenges. In this paper, we propose background subtraction algorithm based on continuous learning of low-rank matrix using image pixels represented on a Minimum Spanning Tree (MST). First, efficient MST is constructed to estimate minimax path among the spatial pixels of input image. Then, robust smoothing constraint is employed on these pixels for outlier removal. The low-rank matrix is updated using MST-based observed pixels. Finally, we apply the markov random field (MRF) to label the absolute value of the sparse error. Our experiments show that the proposed algorithm achieves promising results on dynamic background and camera jitter sequences compared to state-of-the-art methods.