Real-time Pose Estimation for Mobile Devices: A Review
Journal Title: International Journal of Multidisciplinary Research and Analysis - Year 2024, Vol 7, Issue 04
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.
Authors and Affiliations
Paulo V. Opiña Jr. , Arnel C. Fajardo
Keywords
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- EP ID EP734683
- DOI 10.47191/ijmra/v7-i04-44
- Views 47
- Downloads 0
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