TY - CONF T1 - Visual saliency model for robot cameras T2 - IEEE International Conference on Robotics and Automation, 2008. ICRA 2008 Y1 - 2008 A1 - Butko, N. A1 - Zhang, L. A1 - Cottrell, G. A1 - Movellan, J. KW - Application software KW - approximation theory KW - Bayes methods KW - Bayesian methods KW - Bayesian model KW - camera control KW - Cameras KW - Central Processing Unit KW - Computational efficiency KW - Computational modeling KW - Explosions KW - fast approximation KW - human visual attention KW - Humans KW - Open loop systems KW - robot cameras KW - robot vision KW - Robot vision systems KW - robotic application KW - task free conditions KW - visual saliency model AB -

Recent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications. Here we propose a fast approximation to a Bayesian model of visual saliency recently proposed in the literature. The approximation can run in real time on current computers at very little computational cost, leaving plenty of CPU cycles for other tasks. We empirically evaluate the saliency model in the domain of controlling saccades of a camera in social robotics situations. The goal was to orient a camera as quickly as possible toward human faces. We found that this simple general purpose saliency model doubled the success rate of the camera: it captured images of people 70% of the time, when compared to a 35% success rate when the camera was controlled using an open-loop scheme. After 3 saccades (camera movements), the robot was 96% likely to capture at least one person. The results suggest that visual saliency models may provide a useful front end for camera control in robotics applications.

JF - IEEE International Conference on Robotics and Automation, 2008. ICRA 2008 PB - IEEE CY - Pasadena, CA SN - 978-1-4244-1646-2 ER -