用视觉技术分析汽车装配线工人动作，提升产线安全与效率。
A vision-based framework for human behavior understanding in industrial assembly lines
- 通过多相机RGB-D视频与动作捕捉数据，实时估计工人3D姿态与位置。
- 在真实产线环境下，姿势分类准确率高，任务进度监控稳定可靠。
- 公开了CarDA数据集，适合工业行为分析与人因工程研究者使用。
本文提出一种基于视觉的框架，用于捕捉和理解汽车门装配线中的人类行为。该框架利用先进的计算机视觉技术，估计工人的位置与三维姿态，分析工作姿势、动作及任务进展。关键贡献是构建了CarDA数据集，包含在真实汽车制造环境中采集的时序同步多相机RGB-D视频、动作捕捉数据，并标注了基于EAWS的工效学风险评分和装配活动。实验结果表明，该方法在工人姿势分类上表现有效，在装配任务进度监测方面具有鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。