· 2017
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
- 7,358citations
- 2017year
Short summary
A new method uses Part Affinity Fields (PAFs) to detect the 2D pose of multiple people in real-time, achieving first place in the COCO 2016 keypoints challenge and surpassing prior state-of-the-art on the MPII benchmark.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Part Affinity Fields (PAFs) for associating body parts with individuals.
- Architecture jointly learns part locations and associations in a sequential prediction process.
- Achieves real-time performance irrespective of the number of people.
- Won the COCO 2016 keypoints challenge and set a new state-of-the-art on the MPII Multi-Person benchmark.
AI-generated from the title and abstract; the full text is not read.
Abstract
We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science