Track color space-time interest points in video

Color Space-Time Interest Points (CSTIP) are among all the interesting low-level features which can be extracted from videos; they provide an efficient characterization of moving objects. The CSTIP are simple and can be used for video stabilization, camera motion estimation, and object tracking. In this paper, we show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for tracking. To increase the robustness of CSTIP features extraction, we suggest a pre-processing step which is based on a Color Video Decomposition and can decompose the input images into a dynamic color texture and structure components. We compute the new Color Space Time Interest Points (CSTIP) associated to the dynamic color texture components by using the proposed algorithm of the detection of Color Space- Time Interest Points. The point tracker object tracks a set of Color Space-Time Interest Points using the robust Zero-Mean Normalized Cross-Correlation (ZNCC), feature-tracking algorithm. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.