Qualitative Motion Understanding

Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade, searching for solutions to some of these problems; however, it still remains one of the more difficult and challenging areas of computer vision research. __Qualitative Motion Understanding__ describes a qualitative approach to dynamic scene and motion analysis, called DRIVE (Dynamic Reasoning from Integrated Visual Evidence). The DRIVE system addresses the problems of (a) estimating the robot's egomotion, (b) reconstructing the observed 3-D scene structure; and (c) evaluating the motion of individual objects from a sequence of monocular images. The approach is based on the FOE (focus of expansion) concept, but it takes a somewhat unconventional route. The DRIVE system uses a qualitative scene model and a fuzzy focus of expansion to estimate robot motion from visual cues, to detect and track moving objects, and to construct and maintain a global dynamic reference model. Front Matter....Pages i-xiii Introduction....Pages 1-8 Framework For Qualitative Motion Understanding....Pages 9-21 Effects of Camera Motion....Pages 23-35 Decomposing Image Motion....Pages 37-79 The Fuzzy Foe....Pages 81-106 Reasoning about Structure and Motion....Pages 107-128 The Qualitative Scene Model....Pages 129-156 Examples....Pages 157-180 Summary....Pages 181-183 Back Matter....Pages 185-210