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

This paper introduces a vision-based framework and dataset 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. • Comprehensive framework for visual human monitoring in real industrial lines. • Robust 3D human pose estimation and physical ergonomic risk analysis. • Fine-Grained activity recognition using deep models (LSTM, BiLSTM, Transformer). • Introduction of the CarDA Dataset for 3D worker behavior in industrial environment. • Extensive performance evaluation across visual tasks under realistic conditions.