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Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation

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Author: Hofmann, K.M. · Gavrila, D.M.
Institution: TNO Defensie en Veiligheid
Source:2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 20 June - 25 June 2009, Miami, FL. USA, 2214-2221
Identifier: 248198
doi: doi:10.1109/CVPRW.2009.5206508
ISBN: 9781424439935
Article number: 5206508
Keywords: Image processing · Human pose estimation · Computer vision · 3D mapping · Multiple cameras


We present a system for the estimation of unconstrained 3D human upper body movement from multiple cameras. Its main novelty lies in the integration of three components: single-frame pose recovery, temporal integration and model adaptation. Single-frame pose recovery consists of a hypothesis generation stage, where candidate 3D poses are generated based on hierarchical shape matching in the individual camera views. In the subsequent hypothesis verification stage, candidate 3D poses are re-projected to the other camera views and ranked according to a multi-view matching score. Temporal integration consists of computing best trajectories combining a motion model and observations in a Viterbi-style maximum likelihood approach. Poses that lie on the best trajectories are used to generate and adapt a texture model, which in turn enriches the shape component used for pose recovery. We demonstrate that our approach outperforms the state-of-the-art in experiments with large and challenging real-world data from an outdoor setting. The new data set is made public to facilitate benchmarking.