Motion-based Object Detection and Tracking in Color Image Sequence

In this paper we present an algorithm for detecting objects in a sequence of color images taken from a moving camera. The first step of our algorithm is the estimation of motion in the image plane. Instead of calculating optical flow, tracking single points, edges or regions over a sequence of images, we determine the motion of clusters, built by grouping of pixels in a color/position feature space. The second step is a motion-based segmentation, where adjacent clusters with similar trajectories are combined to build object hypotheses. Our application area is vision-based driving assistance. The algorithm has been successfully tested in traffic scenes containing objects, such as cars, motorcycles, and pedestrians.