Abstract
Although manufacturing processes are becoming increasingly automated, many factories still rely on manual operations. In such facilities, there is a strong need to automatically detect human errors by checking whether specified procedures are being followed. For this purpose, studies have been conducted on utilizing deep neural network (DNN) based three-dimensional (3D) human pose-tracking methods to examine work procedures. However, most of these techniques require a high-end computer equipped with a graphics processing unit (GPU). On the other hand, in this study, we adopt MediaPipe Pose, a lightweight pose estimation network provided by Google, to perform pose tracking on a low-end personal computer (PC) to enable such systems to be deployed in small factories. However, MediaPipe Pose cannot track the location of a human body because it estimates poses in a coordinate system with the waist as the origin (that is, in a root‑relative coordinate system). Therefore, in this study, we developed a method to obtain the absolute coordinates of the root with a simple calculation. The results of an experimental evaluation show that the computational load of the proposed approach is negligible, and the repeatability of the estimation sufficed to evaluate a given operator's work on a predetermined working path. Therefore, the proposed methods enables work procedures to be checked using MediaPipe Pose on a low-end PC.
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