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Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection

Emre Dogan 1 Gonen Eren 2 Christian Wolf 1 Eric Lombardi 1 Atilla Baskurt 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose a new method for human pose estimation which leverages information from multiple views to impose a strong prior on articulated pose. The novelty of the method concerns the types of coherence modelled. Consistency is maximised over the different views through different terms modelling classical geometric information (coherence of the resulting poses) as well as appearance information which is modelled as latent variables in the global energy function. Moreover, adequacy of each view is assessed and their contributions are adjusted accordingly. Experiments on the HumanEva and Utrecht multi-person motion datasets show that the proposed method significantly decreases the estimation error compared to single-view results.
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Submitted on : Monday, November 27, 2017 - 2:30:51 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM

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Emre Dogan, Gonen Eren, Christian Wolf, Eric Lombardi, Atilla Baskurt. Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection. IET Computer Vision, 2018, 12 (4), pp.403-411. ⟨10.1049/iet-cvi.2017.0146⟩. ⟨hal-01649345⟩



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