Probabilistic Adaptive Agent Based System for Dynamic State Estimation using Multiple Visual Cues

Most of current machine vision systems suffer from a lack of flexibility to account for the high variability of unstructured environments. Here, as the state of the world evolves the information provided by different visual attributes changes, breaking the initial assumptions of the vision system. This paper describes a new approach for the creation of an adaptive visual system able to selectively combine information from different visual dimensions. Using a probabilistic approach and uncertainty metrics, the system is able to take appropriate decisions about the more relevant visual attributes to consider. The system is based on an intelligent agent paradigm. Each visual algorithm is implemented as an agent, which adapts its behavior according to uncertainty considerations. The proposed system aims to achieve robustness and efficiency. By combining the outputs of multiple vision modules the assumptions and constraints of each module are factored out resulting in a more robust system. Efficiency is achieved through the online selection and specialization of the agents. An implementation of the system for the case of human tracking showed encouraging results.