Confidence-Based Color Modeling for Online Video Segmentation

High quality online video segmentation is a very challenging task. Among various cues to infer the segmentation, the foreground and background color distributions are the most important. However, previous color modeling methods are error-prone when some parts of the foreground and background have similar colors, to address this problem, we propose a novel approach of Confidence-based Color Modeling (CCM). Our approach can adaptively tune the effects of global and per-pixel color models according to the confidence of their predictions, methods of measuring the confidence of both type of models are developed. We also propose an adaptive threshold method for background subtraction that is robust against ambiguous colors. Experiments demonstrate the effectiveness and efficiency of our method in reducing the segmentation errors incurred by ambiguous colors.