Object color categorization in surveillance videos
We address the problem of automatic color categorization of the objects in surveillance videos. This problem is challenging for realistic situations due to the large intra-class variations of the same color and the large portions of noisy areas including the backgrounds and the parts of the objects that do not contribute to color assignments. We develop an integrated color categorization system with algorithms that address these challenges. With the algorithms proposed in this paper, we can improve the average color categorization accuracy by 18% from our previous work [7].