Real-time Pose Estimation for Mobile Devices: A Review

Abstract

Human pose estimation (HPE) is a critical computer vision task that underpins various applications such as motion analysis and human-computer interaction. Deep learning has significantly improved HPE accuracy, but challenges, specifically in limited training data and occlusions remain. This work focuses on real-time HPE on mobile devices. We compare three prominent frameworks (BlazePose, OpenPose, AlphaPose) by analyzing their input data requirements, underlying inference procedures, and performance on popular datasets. We also explore commonly used HPE benchmark datasets and evaluation metrics. This comparative analysis provides a clear understanding of the current state-of-the-art for real-time mobile HPE, benefiting researchers and developers working on mobile applications that leverage HPE functionalities.