Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields
Bahareh Kalantar, Naonori Ueda, Mohsen Zand, Husam A. H. Al-Najjar · 2023
This paper proposes a novel approach for moving object detection in video sequences captured by nonstationary cameras. The approach, called RCMFD, uses region-based correlated motion fields decomposition, which exploits the sparsity of foreground motions against the low-rank structured background motion. The method uses spatial correlations of region-based features to boost accurate change detection for motion estimation, and motion features across object boundaries are used to exploit moving objects. A dense optical field, which is robust to illumination changes and noise, is established using cross-correlation of region-based features, and a robust principal component analysis (RPCA) model is applied to partition exploited motions into background and foreground motions. Experiments demonstrate the robustness of the proposed method on real video sequences.