Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

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, allowi...

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Bibliographic Details
Published in:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1302 - 1310
Main Authors: Zhe Cao, Simon, Tomas, Shih-En Wei, Sheikh, Yaser
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Benchmark testing
Computer architecture
Detectors
Pose estimation
Runtime
Training
Two dimensional displays
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2017.143