Bayesian Spatiotemporal Context Integration Sources in Robot Vision Systems

Abstract

Having as a main motivation the development of robust and high performing robot vision systems that can operate in dynamic environments, we propose a bayesian spatiotemporal context-based vision system for a mobile robot with a mobile camera, which uses three different context-coherence instances: current frame coherence, last frame coherence and high level tracking coherence (coherence with tracked objects). We choose as a first application for this vision system, the detection of static objects in the RoboCup Standard Platform League domain. The system has been validated using real video sequences and has presented satisfactory results. A relevant conclusion is that the last frame coherence appears to be not very important in the tested cases, while the coherence with the tracked objects appears to be the most important context level considered.