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AI-Based Pose Estimation of Human Operators in Manufacturing Environments
Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy.
Department of Industrial and Systems Engineering (ISE), The Hong Kong Polytechnic University, Hong Kong SAR, China.
KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.ORCID iD: 0000-0001-8679-8049
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Nature , 2024, Vol. Part F2256, p. 3-38Chapter in book (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature , 2024. Vol. Part F2256, p. 3-38
Keywords [en]
Computer vision, Human pose estimation, Manual processes, Monitoring
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-344039DOI: 10.1007/978-3-031-54034-9_1Scopus ID: 2-s2.0-85185519584OAI: oai:DiVA.org:kth-344039DiVA, id: diva2:1841409
Note

QC 20240229

Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-29Bibliographically approved

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Wang, Lihui

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