Part Affinity Fields

Year: 2,017
Authors: Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh
Journal:  IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Programming languages: Matlab, Python

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.

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