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

Abstract

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. 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 1 .

Multi-person Pose Estimation with Local Joint-to-Person Associations

“…The validation set is sampled according to the split proposed in [4] for the single person setup, i.e., we chose all multi-person images that are part of the validation test set proposed in [4] and use all other images for training. In addition we compare the proposed method with the state-of-the-art approach [2] on their selected subset of 288 images, and also compare with [5] on the complete test set. The accuracy is measured by average precision (AP) for each joint using the scripts provided by [2].…”

“…The state-of-the-art approaches, however, demonstrate that graphical models are of little importance in the presence of strong part detectors since the long-range relationships of the body parts can be directly incorporated in the part detectors [1][2][3]5,[8][9][10]. In [3,8,9] multi-staged CNN architectures are proposed where each stage of the network takes as input the score maps of all parts from its preceding stage.…”

“…This means that the approach can estimate the poses of at most 7 fully visible persons with 14 body parts per person. Despite the restriction, the inference takes roughly 72 hours for a single image [5]. In [5], the authors build upon the same model and propose to use stronger part detectors and image dependent spatial models along with an incremental optimization approach that significantly reduces the optimization time of [2].…”

“…Despite the restriction, the inference takes roughly 72 hours for a single image [5]. In [5], the authors build upon the same model and propose to use stronger part detectors and image dependent spatial models along with an incremental optimization approach that significantly reduces the optimization time of [2]. The approach, however, is still too slow for practical applications since it requires 8 minutes per image and still limits the number of proposals to a maximum of 150.…”

“…While the model proposed in [2] can be solved by integer linear programming and achieves state-of-the-art results on a very small subset of the MPII Human Pose Dataset, the complexity makes it infeasible for a practical application. As reported in [5], the processing of a single image takes about 72 hours.…”