Head direction estimation from low resolution images with scene adaptation
Computer Vision and Image Understanding
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We present a comprehensive approach to track gaze by estimating location, body pose, and head pose direction of multiple individuals in unconstrained environments. The approach combines person detections from fixed cameras with directional face detections obtained from actively controlled pan tilt zoom (PTZ) cameras. The main contribution of this work is to estimate both body pose and head pose (gaze) direction independently from motion direction, using a combination of sequential Monte Carlo Filtering and MCMC sampling. There are numerous benefits in tracking body pose and gaze in surveillance. It allows to track people's focus of attention, can optimize the control of active cameras for biometric face capture, and can provide better interaction metrics between pairs of people. The availability of gaze and face detection information also improves localization and data association for tracking in crowded environments. The performance of the system will be demonstrated on data captured at a real-time surveillance site.