swMATH ID: 27574
Software Authors: Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
Description: DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model. 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 this http URL: http://pose.mpi-inf.mpg.de/
Homepage: https://arxiv.org/abs/1605.03170
Source Code:  https://github.com/eldar/deepcut
Related Software: MultiPoseNet; PersonLab; RMPE; ImageNet; GSNet; CarFusion; DeepLabCut; Occlusion-Net; LightTrack; HigherHRNet; OpenPose; ApolloCar3D; BlazeFace; SMPL; MOT16; MS-COCO; PyTorch; Python; OpenPifPaf; ISUD
Cited in: 2 Documents

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