Since the beginning of computer vision the detection and computation of motion in image sequences has been of enormous interest. Because of the increasing computational performance, tracking of moving objects in real--time has become more and more important in the past years. There is a wide range of area of applications: Video surveillance, autonomous mobile system, service and cleaning robots, or systems assisting car drivers.
In the past, most such work was based on specialized hardware, like pipelined imaging hardware (DataCube, ), oder transputer systems [8,20]. Only a few systems are known, which deal with tracking of moving objects in real--time without dedicated hardware [1,18].
Besides the use for image segmentation, contour based approaches have been of increasing interest for object tracking, too . Most of the work identifies object boundaries by the image gradient .  uses color and motion information to track cars in complex outdoor scenes.  presents a complex framework of elastic models and stochastic filtering. The image contrast is used to identify the boundary of the object within a search window. A radial representation of the contour for real--time tracking of moving objects has been proposed by . The authors use the optical flow combined with the image gradient to extract the moving object. In contrast to the approach described in this introduction, they have no coupling of contour elements by means of an internal energy. The system runs on a transputer architecture.
In the special case of object tracking by active contours , a lot of work has been done to improve the contour extraction in the presence of heterogeneous background [21,14].  presents an approach for region based active contours, to allow for the segmentation of contours in medical images with weak object contours but unique statistics inside the contour. The problem of tracking contours in image sequences with complex background is the scope of . At present, the computational complexity of both contour based algorithms does not allow for real--time applications.