TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Distributed Real-time 3D Pose Estimation Framework based on Asynchronous Multiviews |
| 영문제목(English Title) |
A Distributed Real-time 3D Pose Estimation Framework based on Asynchronous Multiviews |
| 저자(Author) |
Taemin Hwang
Jieun Kim
Minjoon Kim
|
| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0559 ~ 0575 (2023. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
3D human pose estimation is widely applied in various fields, including action recognition, sports analysis, and human-computer interaction. 3D human pose estimation has achieved significant progress with the introduction of convolutional neural network (CNN). Recently, several researches have proposed the use of multiview approaches to avoid occlusions in single-view approaches. However, as the number of cameras increases, a 3D pose estimation system relying on a CNN may lack in computational resources. In addition, when a single host system uses multiple cameras, the data transition speed becomes inadequate owing to bandwidth limitations. To address this problem, we propose a distributed real-time 3D pose estimation framework based on asynchronous multiple cameras. The proposed framework comprises a central server and multiple edge devices. Each multiple-edge device estimates a 2D human pose from its view and sends it to the central server. Subsequently, the central server synchronizes the received 2D human pose data based on the timestamps. Finally, the central server reconstructs a 3D human pose using geometrical triangulation.We demonstrate that the proposed framework increases the percentage of detected joints and successfully estimates 3D human poses in real-time. |
| 키워드(Keyword) |
Computer vision
edge processing
multiple view geometry
object detection
pose estimation
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| 파일첨부 |
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