Human Pose Estimation (HPE) has become one of the most relevant topics in computer vision research. This technology can be applied in various fields such as video surveillance, medical care, and sports motion analysis. Due to the increasing demand for HPE, many libraries for this technology have been developed in the last 20 years. Since 2017, many HPE algorithms based on skeletal model have been published and packaged into libraries for easy use by researchers.

These libraries are important for researchers who want to integrate them into real-world applications for video surveillance, medical care, and sports motion analysis.

This paper investigates the strengths and weaknesses of four popular HPE advanced human pose recognition libraries that can run on mobile devices: Lightweight OpenPose, PoseNet, MoveNet, and Blase Pose.