@inproceedings{1012,
TITLE = {{DeeperCut}: {A} Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
AUTHOR = {Insafutdinov, Eldar and Pishchulin, Leonid and Andres, Bjoern and Andriluka, Mykhaylo and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-3-319-46465-7},
DOI = {10.1007/978-3-319-46466-4_3},
PUBLISHER = {Springer},
YEAR = {2016},
DATE = {2016},
ABSTRACT = {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},
BOOKTITLE = {Computer Vision -- ECCV 2016},
DEBUG = {author: Matas, Jiri; author: Sebe, Nicu; author: Welling, Max},
EDITOR = {Leibe, Bastian},
PAGES = {34--50},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {9910},
ADDRESS = {Amsterdam, The Netherlands},
}