A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics

Real-time acquisition and analysis of three-dimensional (3D) human body kinematics are essential in many applications. In this paper, we present a real-time photogrammetric system consisting of a stereo pair of red-green-blue (RGB) cameras. The system incorporates a multi-threaded and graphics processing unit (GPU)-accelerated solution for real-time extraction of 3D human kinematics. A deep learning approach is adopted to automatically extract two-dimensional (2D) human body features, which are then converted to 3D features based on photogrammetric processing, including dense image matching and triangulation. The multi-threading scheme and GPU-acceleration enable real-time acquisition and monitoring of 3D human body kinematics. Experimental analysis verified that the system processing rate reached similar to 18 frames per second. The effective detection distance reached 15 m, with a geometric accuracy of better than 1% of the distance within a range of 12 m. The real-time measurement accuracy for human body kinematics ranged from 0.8% to 7.5%. The results suggest that the proposed system is capable of real-time acquisition and monitoring of 3D human kinematics with favorable performance, showing great potential for various applications.

Automated summary

The study introduces a real-time system for acquisition and analysis of 3D human body kinematics based on photogrammetry, utilizing deep learning and GPU acceleration for real-time extraction and processing of kinematic data. Experimental results show that the system achieves high measurement accuracy and effective detection distance at a rate of 18 frames per second.