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.
This work was partially supported by the EPSRC sponsored MEDUSA, PROCESS and REVEAL projects (Grant No. EP/E001025/1, EP/E033288 and GR/S98443/01 respectively).
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Kuo, P., Makris, D., Megherbi, N., Nebel, JC. (2008). Integration of Local Image Cues for Probabilistic 2D Pose Recovery. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_21
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DOI: https://doi.org/10.1007/978-3-540-89646-3_21
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