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Conference Paper

Monocular 3D Pose Estimation and Tracking by Detection

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Citation

Andriluka, M., Roth, S., & Schiele, B. (2010). Monocular 3D Pose Estimation and Tracking by Detection. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (pp. 623-630). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2010.5540156.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-15C6-7
Abstract
Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current methods are able to recover 3D pose for a single person in controlled environments, they are severely challenged by real-world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an initial estimate of the 2D articulation and viewpoint of the person from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust image observations to reliably recover 3D pose. We demonstrate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions.