Active learning system for object fingerprinting

Object fingerprinting and identification is a critical part of effective visual surveillance systems. In this paper, we present an approach to actively learn the object models in order to fingerprint the objects. Our approach uses a view-based classifier cascade that actively learns to recognize the generic class of the object. Salient features unique to the specific instance of the selected class of objects are modeled using fuzzy attribute relational graphs. These graphs are also adapted to represent object information gathered from multiple views. Preliminary results are quite promising and extensive studies are underway to ascertain the use of the system in more complicated scenarios.