TY - GEN
T1 - Real-Time Multi-view 3D Pose Estimation System with Constant Frame Speed
AU - Kim, Minjoon
AU - Hwang, Taemin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Vision-based 3D human pose estimation is a key technique in recognizing human behavior and is widely applied to various fields dealing with human-computer interactions. In particular, the multi-view-based 3D pose recognition method is a method of predicting hypothetical accurate 3D poses that solve problems such as rotation and obscuration by compensating for the shortcomings of viewpoint-dependent 2D pose recognition method and single-view-based 3D pose recognition method. The multi-view-based 3D pose recognition method has excellent prediction performance, but there are difficulties that come because it uses multiple cameras. It is a difficulty in synchronization to simultaneously control an excessive amount of computation at the central server and multiple cameras. In this paper, we propose a distributed real-time 3D pose estimation framework based on asynchronous multi-cameras. The proposed framework consists of a central server and a number of edge devices, which utilize timestamp techniques to output 3d pose estimation results at constant frame speed. Finally, we implement and demonstrate that we successfully estimate a 3D human pose of 30 fps in real time by constructing the proposed framework as a demo platform.
AB - Vision-based 3D human pose estimation is a key technique in recognizing human behavior and is widely applied to various fields dealing with human-computer interactions. In particular, the multi-view-based 3D pose recognition method is a method of predicting hypothetical accurate 3D poses that solve problems such as rotation and obscuration by compensating for the shortcomings of viewpoint-dependent 2D pose recognition method and single-view-based 3D pose recognition method. The multi-view-based 3D pose recognition method has excellent prediction performance, but there are difficulties that come because it uses multiple cameras. It is a difficulty in synchronization to simultaneously control an excessive amount of computation at the central server and multiple cameras. In this paper, we propose a distributed real-time 3D pose estimation framework based on asynchronous multi-cameras. The proposed framework consists of a central server and a number of edge devices, which utilize timestamp techniques to output 3d pose estimation results at constant frame speed. Finally, we implement and demonstrate that we successfully estimate a 3D human pose of 30 fps in real time by constructing the proposed framework as a demo platform.
KW - 3D Pose Estimation
KW - Asynchronous Edge Processing
KW - Human Pose Estimation
KW - Human-Computer Interaction
KW - Real-time Application
UR - https://www.scopus.com/pages/publications/85171326942
U2 - 10.1007/978-3-031-35989-7_32
DO - 10.1007/978-3-031-35989-7_32
M3 - Conference contribution
AN - SCOPUS:85171326942
SN - 9783031359880
T3 - Communications in Computer and Information Science
SP - 250
EP - 255
BT - HCI International 2023 Posters - 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings, Part I
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
A2 - Salvendy, Gavriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Human-Computer Interaction , HCII 2023
Y2 - 23 July 2023 through 28 July 2023
ER -