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Other publications by Jean-Christophe Nebel
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P. Kuo, D. Makris and J.-C. Nebel
Computer Vision and Image Understanding, 115(2), pp 242-255
2011
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Abstract
Cited by
(
Google Scholar: 17
ISI Web of Knowledge: 1
& SCOPUS: 1
): 17
In this paper, we address the recovery of human 2D postures from monocular image sequences. We propose a novel pose estimation framework which is based on the integration of probabilistic bottom-up and top-down processes which iteratively refine each other: foreground pixels are segmented using image cues whereas a hierarchical 2D body model fitting constraints body partitions. Its main advantages are two-fold. First, the presented framework is activity-independent since it does not rely on learning any motion model. Secondly, we propose a confidence score indicating the quality of each estimated pose. Our study also reveals significant discrepancy between ground truth joint positions according to whether they are defined by humans or a motion capture system. Quantitative and qualitative results are presented on a variety of video sequences to validate our approach.
2024