Abstract-This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous 'intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data. Index Terms-Computer vision, context awareness, deep belief networks, head pose estimation, tracking, video signal processing, video surveillance.
Baxter et al. REF extend a Kalman Filter with an instantaneous prior belief about where people will move, based on where they are currently looking at.
An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features
IEEE Signal Processing Letters
IEEE Signal Processing Letters
Computer Science