Integration of Local Image Cues for Probabilistic 2D Pose Recovery

Authors:
Paul Kuo;Dimitrios Makris;Najla Megherbi;Jean-Christophe Nebel
Affiliations:
Digital Imaging Research Centre, Kingston University, London, UK;Digital Imaging Research Centre, Kingston University, London, UK;Applied Mathematics and Computing Group, Cranfield University, UK;Digital Imaging Research Centre, Kingston University, London, UK
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Year:
2008

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Abstract

A novel probabilistic formulation for 2-D human pose recovery from monocular images is proposed. It relies on a bottom-up approach based on an iterative process between clustering and body model fitting. Body parts are segmented from the foreground by clustering a set of images cues. Clustering is driven by 2D human body model fitting to obtain optimal segmentation while the model is resized and its articulated configuration is updated according to the clustering result. This method neither requires a training stage, nor any prior knowledge of poses and appearance as characteristics of body parts are already embedded in the integrated cues. Furthermore, a probabilistic confidence measure is proposed to evaluate the expected accuracy of recovered poses. Experimental results demonstrate the accuracy and robustness of this new algorithm by estimating 2-D human poses from walking sequences.