Agent-based moving object correspondence using differential discriminative diagnosis

We propose a novel method for temporally and spatially corresponding moving objects by automatically learning the relevance of the objects' appearance features to the task of discrimination. Efficient correspondence is achieved by enforcing temporal consistency of the relevances for a particular object. Relevances are learned using a technique we have termed "differential discriminative diagnosis". An agent is assigned to each moving object in the scene. The agent possesses the basic capability to decide whether or not an object in the scene is the one it represents. Each agent customizes itself to the object by means of differential discriminative diagnosis as the object persists in the scene. We explain this correspondence scheme as applied to the task of corresponding moving people in a surveillance system.