Process identification over working video with pose estimation towards quantifying variances in operating events

Abstract

In the era of digitalization, there has been growing interest to utilize real data involved in manufacturing. One challenging issue in manufacturing lies in standardization of human operations like assembly. The working time in them can vary, according to the operation complexity and operator skill. In the following, we call this variance "work deviation''. To quantify it, we address the elementary task to identify a series of assembly processes from video data. In this paper, we first prepare for one benchmark simply capturing normal assembly works. Using the benchmark, we empirically evaluate the proposed method based on pose estimation for identification task. We prepared query about the working time and compared the query results by the manual result and the machine learning model result.