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 ∼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.
Graphics processing unit-based implementation of a one-dimensional novel-look-up-table for real-time computation of Fresnel hologram patterns of three-dimensional objects
