LiveSkeleton: High-Quality Real-Time Human Tracking and Pose Estimation

We introduce LiveSkeleton, a high-performance human pose estimation algorithm designed for real-time multimedia applications. Our approach integrates robust person detection using Scaled-YoloV4, efficient optical-flow based tracking and the high-quality RTMPose algorithm for skeleton extraction. Operating at 25 frames per second for up to five individuals simultaneously, the system demonstrates robust performance across content from diverse scenarios, including VR/XR environments, surveillance contexts, and healthcare settings. Experimental results confirm that the algorithms performs well in challenging conditions, such as crowded scenes with substantial occlusion, thereby offering significant potential for enhancing human-computer interaction and automated behavior analysis.