Learning Foveal Sensing Strategies in Unconstrained Surveillance Environments

In this paper we report on techniques for automatically learning foveal sensing strategies for an active pan-tiltzoom camera. The approach uses reinforcement learning to discover foveal actions maximizing the performance of visual detectors, that are in turn assumed to be highly correlated with the task at hand. In our case, a frontal face detection module is employed to this end. The system learns if, when and how to foveate on a subject, based on its previous experience in terms or successful actions in similar situations. An action is successful if it leads to a correct face detection in the high resolution images obtained when the subject is zoomed in. In contrast with existing methods, the proposed approach obviates the need for camera calibration and camera performance modeling. Also, the method does not rely on active tracking of targets. Experimental results show how the system can be deployed in unconstrained surveillance environments, and is capable of learning foveation str...

• Type: Conference
• Year: 2006
• Where: AVSS
• Authors: Andrew D. Bagdanov, Alberto Del Bimbo, Walter Nunziati, Federico Pernici
• Keywords: Active Pan-tiltzoom Camera, AVSS 2006, Face Detection, Frontal Face Detection, Signal Processing