A Bayesian Framework for Extracting Human Gait using Strong Prior Knoweldge

Zhou, Ziheng and Prugel-Bennett, Adam and Damper, Robert I. (2006) A Bayesian Framework for Extracting Human Gait using Strong Prior Knoweldge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (11). pp. 1738-1752. ISSN 0162-8828. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Extracting full-body motion of walking people from monocular video sequences in complex, real-world environments is an important and difficult problem, going beyond simple tracking, whose satisfactory solution demands an appropriate balance between use of prior knowledge and learning from data. We propose a consistent Bayesian framework for introducing strong prior knowledge into a system for extracting human gait. In this work, the strong prior is built from a simple articulated model having both time-invariant (static) and time-variant (dynamic) parameters. The model is easily modified to cater to situations such as walkers wearing clothing that obscures the limbs. The statistics of the parameters are learned from high-quality (indoor laboratory) data and the Bayesian framework then allows us to "bootstrap" to accurate gait extraction on the noisy images typical of cluttered, outdoor scenes. To achieve automatic fitting, we use a hidden Markov model to detect the phases of images in a walking cycle. We demonstrate our approach on silhouettes extracted from fronto-parallel ("sideways on") sequences of walkers under both high-quality indoor and noisy outdoor conditions. As well as high-quality data with synthetic noise and occlusions added, we also test walkers with rucksacks, skirts, and trench coats. Results are quantified in terms of chamfer distance and average pixel error between automatically extracted body points and corresponding hand-labeled points. No one part of the system is novel in itself, but the overall framework makes it feasible to extract gait from very much poorer quality image sequences than hitherto. This is confirmed by comparing person identification by gait using our method and a well-established baseline recognition algorithm

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications), > TK7880 Applications of electronics (inc industrial & domestic) > TK7885 Computer engineering
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Depositing User: Yiqing Liang
Date Deposited: 16 Mar 2009 10:11
Last Modified: 30 Apr 2014 14:34
Resource URI: https://kar.kent.ac.uk/id/eprint/9053 (The current URI for this page, for reference purposes)
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