Detection And Tracking Of Moving Objects

We present a close to real-time system (4 frames per second) capable to detect and track moving objects in natural color scenes. Our approach is based on the idea of symbolic matching. Complete segments are used for matching instead of matching single pixels or features. Thus, first of all each image has to be segmented. The segmented regions are described by features and matched on a symbolic level from frame to frame. The use of color significantly improves the stability of the segmentation and matching phase. In the matching phase we explicitly take into account that no segmentation algorithm can guarantee that regions will be segmented uniformly stable from frame to frame. This approach has several advantages over traditional differential techniques or feature point matching algorithms: e.g. better performance in the presence of noise, detection of large motion between frames, dense motion vector fields and accurate estimation of motion boundaries