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

TL;DR

This paper presents a vision-based framework utilizing advanced computer vision techniques and a new dataset to analyze and monitor human behavior and ergonomic risks in industrial assembly lines, specifically car door manufacturing.

Contribution

Introduction of CarDA, a comprehensive dataset with multi-modal data for human pose and action analysis in industrial settings, enabling improved behavior understanding and ergonomic assessment.

Findings

Effective classification of worker postures

Robust monitoring of assembly task progress

Demonstrated framework's applicability in real manufacturing environments

Abstract

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.