DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
| Autor: | Mykhaylo Andriluka, Eldar Insafutdinov, Bjoern Andres, Bernt Schiele, Leonid Pishchulin |
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| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry 020207 software engineering 02 engineering and technology Space (commercial competition) Machine learning computer.software_genre 3D pose estimation Task (project management) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business Pose computer |
| Zdroj: | Computer Vision – ECCV 2016 ISBN: 9783319464657 ECCV (6) |
| Popis: | 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. Evaluation is done 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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