Object- and space-based visual attention: An integrated framework for autonomous robots

This paper argues that the object- and space-based modes of visual attention can be naturally integrated in a common mathematical framework. In an earlier work [1] we have proposed a mathematical model of visual attention for robotic system exploiting the knowledge of visual attention mechanism of the primates. This paper investigates on the validity of the proposed model for robotic systems through experimentation on a real robot. The paper sheds light on a number of real world issues involved with the design of visual attention system for physically embodied robots and explains how the proposed Bayesian model of visual attention addresses these issues. The object- and space-based modes of visual attention are naturally integrated in the model and is reflected in the sequential Monte Carlo implementation of the model on a real robot.

Authors

• Momotaz Begum
• George K. I. Mann
• Raymond G. Gosine
• Fakhri Karray

Keywords

• Robots
• Visualization
• Bayesian methods
• Computational modeling
• Image color analysis
• Mathematical model
• Cameras
• Visual Attention
• Framework For Robots
• Bayesian Model
• Attention Mechanism
• Robotic System
• Common Framework
• Attention Model
• Attentional System
• Real Robot
• Visual Attention Mechanism
• Dynamic Model
• Measurement Model
• Computer Vision
• Working Memory
• Focus Of Attention
• Mean Of Distribution
• Shape Features
• Small Weight
• Color Features
• Particle Filter
• Problem Instances
• Overt Attention
• Covert Attention
• Motion Stimuli
• Importance Weights
• Decision Uncertainty
• Red Objects
• Retinal Input
• Perceptual Uncertainty
• Measurement Update