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DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons188120

Insafutdinov,  Eldar
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45196

Pishchulin,  Leonid
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons98382

Andres,  Bjoern
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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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Zitation

Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016). DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model. In B. Leibe (Ed.), Computer Vision -- ECCV 2016 (pp. 34-50). Berlin: Springer. doi:10.1007/978-3-319-46466-4_3.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002A-FCF9-C
Zusammenfassung
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. We evaluate our approach on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de