Active-matting-based object tracking with color cues

In view of drifting issue in object tracking, the conventions are prone to degenerate due to the inaccuracy in appearance models. In this paper, we propose an activematting-based visual tracker to give more precise contours in targets. The basic idea is to explore the biological inspired color surface coding theory to refine the original interest point detector, which benefits for robust representation to extract the suitable interior object areas for matting. In order to generate accurate pixel-wise labels of each frame for matting, we have both foreground and background interest points using k-d trees between two successive frames, under the similar geometric constraints from the object silhouette in the previous frame. The resulting tracker achieves performance competitive with the state-of-the-art in different color video sequences, especially under the scenarios with strong illuminations and posture variations, as well as small-scale targets in the long-time sequence.