A Computatronal Model for Moving Target Detection, Depth and Velocity Estimation

This proposal describes a computational model of human observers, DYTA(dynamic Target analysis), for dynamic target detection, depth and velocity estimation DYTA will be developed based on human perception knowledge as well as physics of moving vehicles. The computational model consists of two levels of processes, early and cognitive visual processes. At the early visual processes level, DYTA has two independent units: Gabor multi-channel filtering to model human early vision processing, and computation of motion vectors of moving vehicels based on the physics of moving vehicles. At the cognitive level, DYTA has a fuzzy learning and inference model for reasoning about the behavior of dynamic targets, such as Location, distance and velocity. The algorithma we will develop in phase I includes computing target location by combining texure and motion features; computing depth and velocity of moving targets using rigid body motion techniques; and performing fuzzy learning and inference. A number of basic research issues will be addressed including moving vechicle detection, 3D structure recovery from image sequences,robust fuzzy learning and inflerence under uncertain and incomplete knowledge. DYTA is designed to predict human observer's response to the dynamic information in a scence,in particular, to moving vehicle location, distance and velocity. BENEFITS: This technology has potential applications in both military and commercial markets. Examples of such applications are military dynamic target detection and acquisition, military and commercial surveillance, a monitoring and warning system in military operational driving, commercial vehicle saftey study, driver's warning system and visual enhancement, highway traffic control. etc