Efficient Human Pose Estimation: Leveraging Advanced Techniques with MediaPipe

TL;DR

This paper enhances human pose estimation using MediaPipe by improving accuracy and efficiency, enabling real-time applications across various fields like AR, sports, and healthcare, especially on mobile and embedded devices.

Contribution

Introduces novel modifications to MediaPipe that significantly boost pose estimation accuracy and computational speed in real-time scenarios.

Findings

Enhanced accuracy in dynamic and occluded scenarios

Significant speed improvements over traditional models

Successful deployment on mobile and embedded systems

Abstract

This study presents significant enhancements in human pose estimation using the MediaPipe framework. The research focuses on improving accuracy, computational efficiency, and real-time processing capabilities by comprehensively optimising the underlying algorithms. Novel modifications are introduced that substantially enhance pose estimation accuracy across challenging scenarios, such as dynamic movements and partial occlusions. The improved framework is benchmarked against traditional models, demonstrating considerable precision and computational speed gains. The advancements have wide-ranging applications in augmented reality, sports analytics, and healthcare, enabling more immersive experiences, refined performance analysis, and advanced patient monitoring. The study also explores the integration of these enhancements within mobile and embedded systems, addressing the need for computational efficiency and broader accessibility. The implications of this research set a new benchmark for real-time human pose estimation technologies and pave the way for future innovations in the field. The implementation code for the paper is available at https://github.com/avhixd/Human_pose_estimation.