Application Of Expert System Techniques To A Visual Tracker
Harley R. Myler, Wiley E. Thompson, Gerald M. Flachs · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1985
A structure for visual tracking system is presented which 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 which supplies segmented image features to a data reduction algorithm which holds the reduced data in a temporary data base relation. This relation is then classified vio two separate modes, learn and track. Under learn mode, an external teacher-irector operator provides identification and weighting cues for membership in a long-term storage relation within a knowledge base. Track mode operates autonomously from the learn mode where the system determines feature validity by applying fuzzy set membership criteria to previously stored track information in the database. Results determined from the classification generate tracker directives which either enhance or permit current tracking to continue or cause the tracker to search for alternate targets based upon analysis of a global target tracking list. The classification algorithm is based on correlative analysis of the tracker's segmented output presentation after low pass filtering derives lower order harmonics of the feature. The fuzzy set membership criteria is based on size, rotation, Irame location, and past history of the feature. The first three factors are lin-ear operations on the spectra, while the last is generated as a context relation in the knowledge base. The context relation interlinks data between features to facilitate tracker operation during feature occlusion or presence of countermeasures.