Biased Competitive Model of Humanoid Visual Attention Using Fuzzy Discrete Event System

This paper presents a novel computational model of robotic visual attention using fuzzy discrete event system (FDES). The method attempts to implement the Biased competitive hypothesis, where the bottom-up bias (or object saliency) is combined with the top-down preference (or specific object bias) for attention modeling. In the proposed implementation of biased competitive hypothesis, FDES is used to model the sensory feedback from the environment to generate the object-saliency. An arbiter combines the top-down bias with the object-saliency and selects an appropriate object for visual attention. The FDES-based architecture provides the opportunity to implement heuristic-based reasoning using the state-transition structure. It also employs fuzzy logic to incorporate deterministic vagueness of human reasoning. The state-based generation of the object saliency prevents abrupt change in visual attention, which helps generating consistent motion commands for the camera. This method uses FDES-based observability and controllability to measure the decision vagueness and sudden change in the visual attention. These measurements are used to modulate the pantile commands of the camera to produces slower speed when the attention is suddenly distracted. Experimental results are also presented to validate different aspects of the proposed system.