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• W2066003542 abstract "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 twofold. 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." @default.
• W2066003542 created "2016-06-24" @default.
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• W2066003542 date "2011-02-01" @default.
• W2066003542 modified "2023-10-18" @default.
• W2066003542 title "Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling" @default.
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• W2066003542 doi "https://doi.org/10.1016/j.cviu.2010.09.001" @default.
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