Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices

Abstract

Pose estimation is a significant strategy that has been actively researched in various fields. For example, the strategy has been adopted for motion capture in moviemaking, and character control in video games. It can also be applied to implement the user interfaces of mobile devices through human poses. Therefore, this paper compares and analyzes four popular pose estimation models, namely, OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder, using pre-classified images. The results show that MoveNet Lightning was the fastest, and OpenPose was the slowest among the four models. But OpenPose was the only model capable of estimating the poses of multiple persons. The accuracies of OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder were 86.2%, 97.6%, 75.1%, and 80.6%, respectively.