Visual observation as reactive learning

Meaningful objects in a scene move with purpose. The ability to induce visual expectations from such purpose is important in visual observation. By regarding the spatio-temporal regularities in the moving patterns of an object in the scene as a network of temporally dependent belief hypothesis, visual expectations can be represented by the most likely combinations of the hypotheses based on updating the network in response to instantaneous visual evidence. A particular type of probabilistic single path Directed Acyclic Graph (DAG) belief network, the Hidden Markov Model (HMM), can be used to represent the “hidden” regularities behind the apparently random moves of an object in a scene and reproduce such regularities as “blind”, therefore, insensitive expectations. By adaptively adjusting such a probabilistic belief network with observed visual evidence instantaneously, a Visual Augmented Hidden Markov Model (VAHMM) can be used to model and produce dynamic expectations of a moving object in the scene. In particular, using tracked moving service vehicles at an airport docking stand as visual cues, we present how a VAHMM can be constructed first to represent the probabilistic spatial dependent relationships in the typical moving patterns of a type of vehicle, and then to adjust the weighting parameters of such dependencies dynamically with instantaneous new visual evidence. We describe the use of such model to generate in time the probabilistic expectations of an observed object and discuss some possible initial applications of such a framework for providing selective attention in visual observation.