Human Pose Estimation Using LeYOLO-Nano Architecture

Abstract —Human pose estimation is essential in numerous practical applications, particularly in scenarios demanding fast processing and optimal resource usage, such as surveillance, human with computer interaction, and robotic systems. This technology aims to detect and analyze human body keypoints in images or videos, which is a critical step in understanding an individual's movements and behaviors. This article evaluates the performance of the original LeYOLO-Nano architecture, a lightweight variant of the YOLO architecture, in the application of human body keypoint detection for the purpose of pose estimation. Using MSCOCO2017 dataset, which includes a wide range of real-world conditions, this model achieved a mAP50 of 0.69 and a mAP50:95 of 0.362, demonstrating its ability to detect human poses with adequate accuracy. Moreover, the model is capable of handling data at a rate of 20.78 frames per second using a standard CPU, highlighting its effectiveness for real-time use on edge devices with restricted computing power. LeYOLO-Nano enables efficient human pose estimation on low-power devices, with the potential to further optimize speed and accuracy in real-world applications. Key words—Human pose estimation, keypoint detection, LeYOLO, real-time vision