Demand-Driven Visual Information Acquisition

Fast, reliable and demand-driven acquisition of visual information is the key to represent visual scenes efficiently. To achieve this efficiency, a cognitive vision system must plan the utilization of its processing resources to acquire only information relevant for the task. Here, the incorporation of long-term knowledge plays a major role on deciding which information to gather. In this paper, we present a first approach to make use of the knowledge about the world and its structure to plan visual actions. We propose a method to schedule those visual actions to allow for a fast discrimination between objects that are relevant or irrelevant for the task. By doing so, we are able to reduce the system's computational demand. A first evaluation of our ideas is given using a proof-of-concept implementation.