期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2023-08-31 |
| 作品名稱 | Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network |
| 作品名稱（其他語言） | |
| 著者 | Chien-Chang Chen 1 , Cheng-Shian Lin 1,*, Yen-Ting Chen 1 , Wen-Her Chen 2 , Chien-Hua Chen 2,3 and I-Cheng Chen 2 |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | J. Imaging 2023, 9(9), 181 |
| 摘要 | Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience. The aim of our work is to use a graph-matching network to analyze the similarity in rowers’ rowing posture and further pair rowers to improve the performance of their rowing team. This study proposed a novel video-based performance analysis system to analyze paired rowers using a graph-matching network. The proposed system first detected human joint points, as acquired from the OpenPose system, and then the graph embedding model and graph-matching network model were applied to analyze similarities in rowing postures between paired rowers. When analyzing the postures of the paired rowers, the proposed system detected the same starting point of their rowing postures to achieve more accurate pairing results. Finally, variations in the similarities were displayed using the proposed time-period similarity processing. The experimental results show that the proposed time-period similarity processing of the 2D graph-embedding model (GEM) had the best pairing results. |
| 關鍵字 | OpenPose;graph neural network |
| 語言 | en |
| ISSN | 2313-433X |
| 期刊性質 | 國外 |
| 收錄於 | ESCI NotTSSCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | SWE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/124548 ) |