Moving Object Detection Based on Hierarchical Robust Principal Component Analysis

It is hard for robust principal component analysis (RPCA) to effectively use the spatialtemporal continuity priors of moving objects, thus the RPCA-based model tends to misclassify the dynamic details in the background due to the moving target problem.In this paper, a moving object detection method based on hierarchical RPCA is proposed.The first-pass RPCA rapidly identifies the likely regions of foreground in the down-sampled video sequence.A 3D spatial-temporal total variation model filters the unstructured background disturbance in sparse component.Motion salience map is then generated based on the motion saliency of these foreground regions.The second-pass builds a weighted RPCA model, which imposes a weighted threshold for candidate moving targets.The weighted RPCA model makes the foreground detection robust and can obtain clear and complete foreground.Experimental results show that this method can effectively handle the moving target detection problem under complex dynamic background.