AI-Based Pose Estimation of Human Operators in Manufacturing Environments

Marcello Urgo; Francesco Berardinucci; Pai Zheng; Lihui Wang

doi:10.1007/978-3-031-54034-9_1

AI-Based Pose Estimation of Human Operators in Manufacturing Environments

Marcello Urgo, Francesco Berardinucci, Pai Zheng, Lihui Wang

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

5 Citations (Scopus)

Abstract

The fast development of AI-based approaches for image recognition has driven the availability of fast and reliable tools for identifying the human body in captured videos (both 2D and 3D). This has increased the feasibility and effectiveness of approaches for human pose estimation in industrial environments. This essay will cover different approaches for estimating the human pose based on neural networks (e.g., CNN, LSTM, etc.), addressing the workflow and requirements for their implementation and use. A brief analysis and comparison of the existing AI-based frameworks and approaches will be carried out (e.g. OpenPose, MediaPipe) together with a listing of the related hardware and software requirements. Finally, two case studies presenting applications in the manufacturing sector are provided.

Original language	English
Title of host publication	Lecture Notes in Mechanical Engineering
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-38
Number of pages	36
DOIs	https://doi.org/10.1007/978-3-031-54034-9_1
Publication status	Published - 2 Feb 2024

Publication series

Name	Lecture Notes in Mechanical Engineering
Volume	Part F2256
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Keywords

Computer vision
Human pose estimation
Manual processes
Monitoring

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

More information

10.1007/978-3-031-54034-9_1

Cite this

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AI-Based Pose Estimation of Human Operators in Manufacturing Environments. / Urgo, Marcello; Berardinucci, Francesco; Zheng, Pai et al.
Lecture Notes in Mechanical Engineering. Springer Science and Business Media Deutschland GmbH, 2024. p. 3-38 (Lecture Notes in Mechanical Engineering; Vol. Part F2256).

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

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AI-Based Pose Estimation of Human Operators in Manufacturing Environments

Abstract

Publication series

Keywords

ASJC Scopus subject areas

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