Knowledge-Driven Saliency: Attention to the Unseen

This paper deals with attention in 3D environments based upon knowledge-driven cues. Using learned 3D scenes as top-down influence, the proposed system is able to mark high saliency to locations occupied by objects that are new, changed, or even missing from their location as compared to the already learned situation. The proposal addresses a system level solution covering learning of 3D objects and scenes using visual, range and odometry sensors, storage of spatial knowledge using multiple-view theory from psychology, and validation of scenes using recognized objects as anchors. The proposed system is designed to handle the complex scenarios of recognition with partially visible objects during revisit to the scene from an arbitrary direction. Simulation results have shown success of the designed methodology under ideal sensor readings from range and odometry sensors.