Knowledge base enhancement of visual tracking

A proposed system for a visual tracker is presented that relies on information developed from previous tracking scenarios stored in a knowledge base to enhance tracking performance. The system is comprised of a centroid tracker front end that supplies segmented image features to a data reduction algorithm and subsequently to a track processor operating under one of two modes, learn or track. While in learn mode, a human operator provides identification cues for membership in a long-term storage relation within a knowledge base. In track mode the system operates autonomously with a cognitive processing algorithm replacing the human operator. The autonomous system functions as a correlation tracker by comparing processed input to data that was stored in the knowledge base during learn mode. Results determined from the classification generate tracker directives that either enhance the current track or cause the tracker to search for alternate targets based on a global target tracking list.