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 this https URL

该研究通过优化算法、提高准确性、计算效率和实时处理能力，在人体姿势估计方面取得了重要的进展；改进后的框架在动态运动和部分遮挡等复杂场景下显著提高了准确性，在增强现实、体育分析和医疗保健等方面具有广泛的应用，同时还探索了将这些改进应用于移动和嵌入式系统中，以提高计算效率和扩大可访问性；该研究为实时人体姿势估计技术确立了新的标准，并为未来的创新铺平了道路。

高效的人体姿势估计：利用 MediaPipe 中的先进技术

Efficient Human Pose Estimation: Leveraging Advanced Techniques with  MediaPipe

Building applications that perceive the world around them is challenging. A
developer needs to (a) select and develop corresponding machine learning
algorithms and models, (b) build a series of prototypes and demos, (c) balance
resource consumption against the quality of the solutions, and finally (d)
identify and mitigate problematic cases. The MediaPipe framework addresses all
of these challenges. A developer can use MediaPipe to build prototypes by
combining existing perception components, to advance them to polished
cross-platform applications and measure system performance and resource
consumption on target platforms. We show that these features enable a developer
to focus on the algorithm or model development and use MediaPipe as an
environment for iteratively improving their application with results
reproducible across different devices and platforms. MediaPipe will be
open-sourced at this https URL

MediaPipe 框架可帮助开发人员构建基于算法和模型发展的原型和实现，从而促进智能化应用的迭代改进和跨平台性能测量。