Abstract. This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.
For example, REF computed the likelihood of a moving object based on a motion metric computed from optical flow and then segment the moving objects.
Moving Object Segmentation using Optical Flow and Depth Information
ICRA
Computer Science