Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields

Bahareh Kalantar, Naonori Ueda, Mohsen Zand, Husam Abdulrasool H. Al-Najjar

Abstract: 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.

External IDs:dblp:conf/igarss/KalantarUZA23