Robust tracking based on Boosted Color Soft Segmentation and ICA-R
Fan Yang, Huchuan Lu, Yen‐Wei Chen · 2010
In this paper, we propose a novel approach for robust visual tracking. To separate the foreground from the background, we propose a novel Boosted Color Soft Segmentation (BCSS) algorithm and incorporate Independent Component Analysis with Reference (ICA-R) into the tracking framework. In addition, we design a scheme to fuse and update BCSS and ICA-R. We also propose adaptive scale of tracking window to handle objects' scale changes. Experiments shows that our approach is more robust than some popular tracking systems.