survey

Deep Learning-based Human Pose Estimation: A Survey

Authors:

Taojiannan Yang,

Nasser Kehtarnavaz,

Mubarak ShahAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 1

Article No.: 11, Pages 1 - 37

https://doi.org/10.1145/3603618

Published: 26 August 2023 Publication History

Abstract

Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey article is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 260 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. A regularly updated project page is provided: https://github.com/zczcwh/DL-HPE.

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Index Terms

Deep Learning-based Human Pose Estimation: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 1

January 2024

918 pages

EISSN:1557-7341

DOI:10.1145/3613490

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 26 August 2023

Online AM: 09 June 2023

Accepted: 17 May 2023

Revised: 01 April 2023

Received: 23 January 2022

Published in CSUR Volume 56, Issue 1

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https://doi.org/10.1016/j.engappai.2024.109473
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