Multi-Feature Fusion Based Background Subtraction For Video Sequences With Strong Background Changes
Résumé
Current background subtraction algorithms are sensitive to sudden changes. In this paper, we propose a multi-feature fusion scheme to background subtraction for video sequences with strong background changes. We reconstruct the whole videos frame by frame by fusing several video features. In this fusing step, we design an energy function based on enforcing every features with an equal weight. By comparing reconstruction videos with the original videos, pixels with small differences are classified as background pixels. Thus, we can identify background areas in advance and then we construct a contour-based mask combining mechanism. Experimental results conducted on the OTCBVS, BMC 2012 and PETS 2001 datasets show that our method improves the performance of the Zivkovic’s GMM and SubSENSE for video sequences with strong background changes.