In this paper, we present a novel algorithm to extract a quaternion from a two dimensional camera frame for estimating a contained human skeletal pose. The problem of pose estimation is usually tackled through the usage of stereo cameras and intertial measurement units for obtaining depth and euclidean distance for measurement of points in 3D space. However, the usage of these devices comes with a high signal processing latency as well as a significant monetary cost. By making use of MediaPipe, a framework for building perception pipelines for human pose estimation, the proposed algorithm extracts a quaternion from a 2-D frame capturing an image of a human object at a sub-fifty millisecond latency while also being capable of deployment at edges with a single camera frame and a generally low computational resource availability, especially for use cases involving last-minute detection and reaction by autonomous robots. The algorithm seeks to bypass the funding barrier and improve accessibility for robotics researchers involved in designing control systems.

翻译：本文提出了一种新颖算法，可从二维相机帧中提取四元数，用于估计受限人体骨骼姿态。姿态估计问题通常通过立体相机和惯性测量单元解决，以获取深度和欧氏距离来测量三维空间中的点。然而，使用这些设备会带来较高的信号处理延迟以及显著的资金成本。通过利用MediaPipe（一种用于构建人体姿态估计感知管道的框架），所提算法可从捕获人体对象图像的二维帧中提取四元数，延迟低于五十毫秒，同时可部署在仅使用单目相机帧且计算资源通常较低的边缘设备上，尤其适用于自主机器人需要即时检测与反应的场景。该算法旨在突破资金壁垒，提高机器人控制领域研究人员的研究可及性。