Unsupervised workflow discovery in industrial environments

In this work, we present an approach for the automatic discovery of workflows in industrial environments. In such cluttered scenes, one faces many challenges, which limit the use of state-of-the-art object detection and tracking methods. Instead we propose a purely data-driven method which exploits the temporal structure of the workflow. Our robust technique is free of human intervention and does not need parameter tuning. We show results on two camera views of a working cell in a car assembly line. Workflows are extracted robustly, they match well across the camera views and they are conform with human annotation. Furthermore, we show a simple but efficient extension to analyze the image stream in real time. This assures a smooth running of the workflow and enables the notification of different types of unexpected scenarios.