A Comparative Study on Temporal Action Segmentation for Automobile Assembly Work Videos

Abstract

In recent years, there has been a growing demand for analysis of worker behavior from the viewpoint of labor shortage and improved work efficiency in factories such as automobile assembly. Behavioral analysis of assembly operations makes it possible to automate the measurement of the time required for each task and to confirm that operators are following the same procedures as in the manual. Due to this growing demand, temporal action segmentation using Deep Neural Networks (DNNs) has been widely studied as a new behavior analysis technology. On the other hand, standard benchmark datasets for temporal action segmentation often have a person in action or an object accompanying the action occupying a large area within the viewing angle of the video. On the contrary, videos of automobile assembly operations show a moving automobile that is larger than a worker, which may interfere with the analysis of the workers’ behavior. Therefore, this study applies several existing temporal action segmentation methods to this problem and verifies their effectiveness. Experimental results suggested the possibility of automating behavior analysis in automobile assembly operations.