TY - JOUR
T1 - A real-time photogrammetric system for acquisition and monitoring of three-dimensional human body kinematics
AU - Chen, Long
AU - Wu, Bo
AU - Li, Yuan
N1 - Funding Information:
This work was supported by grants from the Hong Kong Polytechnic University (Project No. 1-ZVN6) and the National Natural Science Foundation of China (Project No. 41671426).
Publisher Copyright:
© 2021 American Society for Photogrammetry and Remote Sensing.
PY - 2021/5
Y1 - 2021/5
N2 - 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 perfor-mance, showing great potential for various applications. DIP: 125.17.16.9.
AB - 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 perfor-mance, showing great potential for various applications. DIP: 125.17.16.9.
UR - http://www.scopus.com/inward/record.url?scp=85111578650&partnerID=8YFLogxK
U2 - 10.14358/PERS.87.5.363
DO - 10.14358/PERS.87.5.363
M3 - Journal article
SN - 0099-1112
VL - 87
SP - 363
EP - 373
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 5
ER -