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

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

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© The Institution of Engineering and Technology
Received 10/03/2017, Accepted 25/11/2017, Revised 09/10/2017, Published 01/12/2017

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.

Inspec keywords: pose estimation

Other keywords: adaptive viewpoint selection; Utrecht multiperson motion; HumanEva; human pose estimation; multiview pose estimation; geometric information

Subjects: Image recognition; Computer vision and image processing techniques

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