An adaptive motion model for person tracking with instantaneous head-pose features

Rolf Baxter, Michael Leach, Sankha Subhra Mukherjee, Neil Robertson

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
139 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)578-582
Number of pages5
JournalIEEE Signal Processing Letters
Issue number5
Early online date22 Oct 2014
Publication statusPublished - 22 Oct 2014


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