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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
A probabilistic model of overt visual attention for cognitive robots.
Momotaz Begum1, Fakhri Karray, George K I Mann
1Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada. m2begum@engmail.uwaterloo.ca
Summary
This study introduces a novel Bayesian model for robotic visual attention, enhancing robot-human interaction. The model helps robots focus on relevant stimuli during head movements, improving their function as cognitive companions.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Robotic visual attention is crucial for cognitive companions.
- Overt attention, involving head and eye movements, presents unique challenges not covered by classical models.
- Head movements in robots disrupt visual field perception and stimulus identification.
Purpose of the Study:
- To propose a robot-centric Bayesian model for overt visual attention.
- To guide robots in directing their camera towards behaviorally relevant and visually demanding stimuli.
- To address the specific challenges of overt attention with head movement in robotic systems.
Main Methods:
- Development of a Bayesian model inspired by primate visual attention mechanisms.
- Implementation using a particle filter to handle the dynamics of head movement.
- Experimental validation of the proposed model's performance.
Main Results:
- The proposed Bayesian model effectively guides robotic visual attention.
- The particle filter implementation successfully addresses challenges associated with head movements.
- Experimental results confirm the model's capability in directing attention to relevant stimuli.
Conclusions:
- The developed Bayesian model offers a robust solution for overt visual attention in robots.
- This advancement is key for robots acting as effective cognitive companions.
- The model's ability to handle head movements enhances its practical applicability in human-robot interaction.
