survey

Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models

Authors:

Luke K. Topham,

Dhiya Al-Jumeily,

Abir HussainAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 6

Article No.: 120, Pages 1 - 42

https://doi.org/10.1145/3533384

Published: 07 December 2022 Publication History

Abstract

Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need for high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that makes the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

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Yang JPark K(2024)Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone LocationBioengineering10.3390/bioengineering1102014111:2(141)Online publication date: 30-Jan-2024
https://doi.org/10.3390/bioengineering11020141
de Souza Cardoso LSchwandt TBroll W(2024)μPose: Synthetic Dataset for Human Pose Estimation in Microgravity Environments2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00282(1557-1562)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00282
Mazurek KBarnard LBotha HChristianson TGraff-Radford JPetersen RVemuri PWindham BJones DAli F(2024)A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkwayScientific Reports10.1038/s41598-024-68402-x14:1Online publication date: 29-Jul-2024
https://doi.org/10.1038/s41598-024-68402-x
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Index Terms

Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
2. Security and privacy

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 6

June 2023

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567471

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2022

Online AM: 15 May 2022

Accepted: 23 April 2022

Received: 01 November 2021

Published in CSUR Volume 55, Issue 6

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Cited By

Yang JPark K(2024)Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone LocationBioengineering10.3390/bioengineering1102014111:2(141)Online publication date: 30-Jan-2024
https://doi.org/10.3390/bioengineering11020141
de Souza Cardoso LSchwandt TBroll W(2024)μPose: Synthetic Dataset for Human Pose Estimation in Microgravity Environments2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00282(1557-1562)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00282
Mazurek KBarnard LBotha HChristianson TGraff-Radford JPetersen RVemuri PWindham BJones DAli F(2024)A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkwayScientific Reports10.1038/s41598-024-68402-x14:1Online publication date: 29-Jul-2024
https://doi.org/10.1038/s41598-024-68402-x
Sethi DSharma DGupta KSrivastava G(2024)SAGA: Stability-Aware Gait Analysis in constraint-free environmentsGait & Posture10.1016/j.gaitpost.2024.06.010113(215-223)Online publication date: Sep-2024
https://doi.org/10.1016/j.gaitpost.2024.06.010
Ray AKolekar M(2024)Transfer learning and its extensive appositeness in human activity recognition: A surveyExpert Systems with Applications10.1016/j.eswa.2023.122538240(122538)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122538
P. SUdgata S(2024)Wi-Fi sensing based person identification and activity recognition using two-phase deep learning modelEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107904132:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.107904
Jassim MAbd DOmri M(2024)Machine learning-based opinion extraction approach from movie reviews for sentiment analysisMultimedia Tools and Applications10.1007/s11042-024-19821-5Online publication date: 10-Jul-2024
https://doi.org/10.1007/s11042-024-19821-5
Zhang LZhao LYan Y(2023)A hybrid neural network-based intelligent body posture estimation system in sports scenesMathematical Biosciences and Engineering10.3934/mbe.202404221:1(1017-1037)Online publication date: 2023
https://doi.org/10.3934/mbe.2024042
Wei SWu Z(2023)The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic ReviewSensors10.3390/s2318766723:18(7667)Online publication date: 5-Sep-2023
https://doi.org/10.3390/s23187667
Samkari EArif MAlghamdi MAl Ghamdi M(2023)Human Pose Estimation Using Deep Learning: A Systematic Literature ReviewMachine Learning and Knowledge Extraction10.3390/make50400815:4(1612-1659)Online publication date: 13-Nov-2023
https://doi.org/10.3390/make5040081
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