AI-Based Pose Estimation of Human Operators in Manufacturing Environments

Marcello Urgo, Francesco Berardinucci, Pai Zheng, Lihui Wang

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

1 Citation (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 languageEnglish
Title of host publicationLecture Notes in Mechanical Engineering
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-38
Number of pages36
DOIs
Publication statusPublished - 2 Feb 2024

Publication series

NameLecture Notes in Mechanical Engineering
VolumePart 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

Fingerprint

Dive into the research topics of 'AI-Based Pose Estimation of Human Operators in Manufacturing Environments'. Together they form a unique fingerprint.

Cite this