International Journal of Advanced Networking and Applications

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Realtime Multi-Person 2D Pose Estimation


Affiliations
1 Department of Information systems, Helwan University – Cairo, Egypt
2 Department of Computer science, Helwan University – Cairo, Egypt
 

This paper explains how to detect the 2D pose of multiple people in an image. We use in this paper Part Affinity Fields for Part Association (It is non-parametric representation), Confidence Maps for Part Detection, Multi-Person Parsing using PAFs, Simultaneous Detection and Association, this method achieve high accuracy and performance regardless the number of people in the image. This architecture placed first within the inaugural COCO 2016 key points challenge. Also, this architecture exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Keywords

Real Time Performance, Part Affinity Fields, Part Detection, Multi-person Parsing, Confidence Maps.
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  • Realtime Multi-Person 2D Pose Estimation

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Authors

Mona Nasr
Department of Information systems, Helwan University – Cairo, Egypt
Rana Osama
Department of Computer science, Helwan University – Cairo, Egypt
Hussein Ayman
Department of Computer science, Helwan University – Cairo, Egypt
Nouran Mosaad
Department of Computer science, Helwan University – Cairo, Egypt
Nourhan Ebrahim
Department of Computer science, Helwan University – Cairo, Egypt
Adriana mounir
Department of Information systems, Helwan University – Cairo, Egypt

Abstract


This paper explains how to detect the 2D pose of multiple people in an image. We use in this paper Part Affinity Fields for Part Association (It is non-parametric representation), Confidence Maps for Part Detection, Multi-Person Parsing using PAFs, Simultaneous Detection and Association, this method achieve high accuracy and performance regardless the number of people in the image. This architecture placed first within the inaugural COCO 2016 key points challenge. Also, this architecture exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Keywords


Real Time Performance, Part Affinity Fields, Part Detection, Multi-person Parsing, Confidence Maps.

References