Vision-based Time-series Worker Activity Recognition

Abstract

This paper presents a study of worker activity recognition that recognizes the time series of worker activities from videos. Our approach adopts pose estimation to detect worker postures from videos and applies an action segmentation model to the estimated worker postures to reduce over-segmentation errors. The result of experiments using newly created simulated datasets revealed that high-accurate time series recognition of worker activities is possible while reducing over-segmentation errors by applying an action segmentation model.