Multi-Person Pose Estimation with Local Joint-to-Person Associations
arXiv:1608.08526
Abstract
Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.
arXiv commentAccepted to European Conference on Computer Vision (ECCV) Workshops, Crowd Understanding, 2016
References in corpus (5)
Cited by in corpus (11)
- Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
- AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
- Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
- Towards Accurate Multi-person Pose Estimation in the Wild
- RMPE: Regional Multi-person Pose Estimation
- Joint Flow: Temporal Flow Fields for Multi Person Tracking
- PoseTrack: Joint Multi-Person Pose Estimation and Tracking
- EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
- Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation
- Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
- A Global to Local Double Embedding Method for Multi-person Pose Estimation