mlsanityData to 7 Oct 2026

A vision-based framework for human behavior understanding in industrial assembly lines

arXiv 2409.17356 ↗First posted 25 Sep 2024cs.CVcited by 1

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Abstract (arXiv)

This paper introduces a vision-based framework for capturing and understanding human behavior in industrial assembly lines, focusing on car door manufacturing. The framework leverages advanced computer vision techniques to estimate workers' locations and 3D poses and analyze work postures, actions, and task progress. A key contribution is the introduction of the CarDA dataset, which contains domain-relevant assembly actions captured in a realistic setting to support the analysis of the framework for human pose and action analysis. The dataset comprises time-synchronized multi-camera RGB-D videos, motion capture data recorded in a real car manufacturing environment, and annotations for EAWS-based ergonomic risk scores and assembly activities. Experimental results demonstrate the effectiveness of the proposed approach in classifying worker postures and robust performance in monitoring assembly task progress.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 37 of its 69 references that are in the map. First posted on arXiv 25 Sep 2024.

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37 of 69 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026HEIR: Learning Human-Entity Interactions with Functional Rolescited by 0