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題名:
Badminton Coach AI:基於深度學習之羽球賽事資訊分析平臺
書刊名:
體育學報
作者:
王威堯
/
張凱翔
/
陳霆峰
/
王志全
/
彭文志
/
易志偉
作者(外文):
Wang, Wei-yao
/
Chang, Kai-shiang
/
Chen, Teng-fong
/
Wang, Chih-chuan
/
Peng, Wen-chih
/
Yi, Chih-wei
出版日期:
2020
卷期:
53:2
頁次:
頁201-213
主題關鍵詞:
機器學習
;
資料探勘
;
電腦視覺
;
大數據分析
;
Machine learning
;
Data mining
;
Computer vision
;
Big data analysis
原始連結:
連回原系統網址
相關次數:
被引用次數:期刊(
3
) 博士論文(0) 專書(0) 專書論文(0)
排除自我引用:
3
共同引用:
53
點閱:7
緒論:科技結合體育是近年來的重要方向,其中透過大數據的蒐集與分析,可以有效瞭解運動員本身或對手的動作、跑位、戰術、以及比賽狀況等重要資訊,有效協助教練或運動員在訓練實務與競賽表現參考。然而,過去主要以人工標記分析方法進行羽球競賽資訊的蒐集,需要投入大量人力和時間成本,而無法有效且快速分析大量競賽影片,使得競賽資訊取得的立即性和精準度受到限制。因此,本研究目的為建構一套完整的大數據羽球分析平台Badminton Coach AI,進行賽局數據蒐集,進一步分析出客觀且具立即性的賽況資訊,供教練與運動員參考。方法:透過深度學習模組TrackNet、YOLOv3、OpenPose、影像處理技術與自行開發之演算法來偵測辨識球體、球員位置與球員骨架姿態等。結果:雛型系統包含四個模組:資料預處理模組、物件偵測與分割模組、數據分析與統計模組與視覺化模組,開發的技術能有效且精準的偵測到羽球飛行軌跡、擊球點、分割回合、得失分原因、主被動擊球與球種分類。結論:本研究已建置出一套具有效能的羽球競賽資訊分析平台Badminton Coach AI。據此研究結果,提出具體建議與研究方向,供實務訓練、競賽之運用與未來研究之參考。
以文找文
Introduction: In recent years, information and communication technology has been widely utilized in sports science research and applications and has become a mainstream tool in sports learning. Microscopic data collection incorporated with big data analysis is an efficient and effective approach for assessing the performance of professional athletes in terms of factors such as motion correctness, movement agility, and tactical analysis. Massive data collection is essential for big data analysis. However, manual labeling is exhausting and time consuming, and in the past, this hindered the development of related research and applications. It should be noted, meanwhile, that computer vision has been applied in posture analysis and injury detection. In this work, deep learning and machine learning techniques are introduced to analyze broadcast video of badminton games in order to develop an automatic data collection system. Using the developed methods, it is possible to extract microscopic data from match videos in a timely manner right after the videos becomes available. As such, a data collection and tactical analysis platform for post-game reviews and tactical analyses called Badminton Coach AI was successfully developed using computer vision based data collection. Methods: Deep learning networks, including TrackNet, YOLOv3, and OpenPose, were adopted and applied to detect the shuttlecock, locate players, and predict player skeletons in every frame of the video images. Results: Four modules are proposed in this work, including the data preprocessing module, feature extraction and segmentation module, statistical analysis module, and visualization module. The developed prototype system can effectively and accurately depict shuttlecock trajectories, detect hit points, segment rallies, judge scores, and classify active or passive shots as well as stroke types. Conclusion: In this study, a complete, effective, and accurate badminton match information analysis platform called Badminton Coach AI was built. The results of the study can not only serve as a reference regarding tactics and training but also indicate potential directions for future research.
以文找文
期刊論文
1.
紀世清、彭俊秀、姚漢禱(20100600)。羽球單打比賽戰術類型研究之效度與信度。休閒運動健康評論,1(2),33-43。
延伸查詢
2.
鄭健民、蔡佳欣、紀世清(20130300)。男子羽球雙打比賽戰術分析。運動教練科學,29,45-57。
延伸查詢
3.
相子元、石又、何金山(20120300)。感測科技於運動健康科學之應用。體育學報,45(1),1-12。
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4.
程紹同(2016)。運動產業4.0時代之大數據新思維。運動管理季刊,33,19-44。
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5.
蔡佩玲、廖焜福、陳志一(20160900)。2011世界羽球錦標賽之女子雙打羽球技術分析研究。臺大體育學報,31,1-8。
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6.
陳儷今、廖莉安(20181200)。羽球男子雙打得分關鍵因素之分析。大專體育學刊,20(4),322-333。
延伸查詢
7.
江佩欣、廖焜福、陳志一(20160300)。女子羽球單打比賽技術分析--以2013年馬來西亞羽球公開賽為例。臺大體育學報,30,1-9。
延伸查詢
8.
許懷中、黃致豪(20171200)。以大數據分析球員技術面表現、對戰組合與中華職棒歷年票房之相關性。體育學報,50(特刊),79-90。
延伸查詢
9.
廖偉喆、李光武(20160300)。世界級女子羽球單打選手發球與接發球技術分析以戴資穎為例。海峽兩岸體育研究學報,10(1),75-84。
延伸查詢
10.
Archana, M.、Geetha, M. K.(2015)。Object detection and tracking based on trajectory in broadcast tennis video。Procedia Computer Science,58,225-232。
11.
Liu, Y.、Han, J.、Xiao, L.(2017)。Analysis of badminton training strategy based on computer intelligent control。Boletin Tecnico,55(20),289-295。
12.
Lin, Y. J.、Weng, S. K.(2018)。Trajectory estimation of the players and shuttlecock for the broadcast badminton videos。IEICE Transactions on Fimdamentals of Electronics, Communications and Computer Sciences,E101A(10),1730-1734。
13.
Zhao, Z.-Q.、Zheng, P.、Xu, S.-T.、Wu, X.(2018)。Object detection with deep learning: A review。IEEE Transactions on Neural Networks and Learning Systems,30(11),3212-3232。
14.
Shishido, H.、Kameda, Y.、Ohta, Y.、Kitahara, I.(2017)。Visual tracking method of a quick and anomalously moving badminton shuttlecock。ITE Transactions on Media Technology and Applications,5(3),110-120。
15.
張簡旭芳、李尹鑫、相子元(20160600)。穿戴科技於運動科學之應用。中華體育季刊,30(2)=117,121-127。
延伸查詢
會議論文
1.
Chen, T. C.、Guestrin, C.(2016)。Xgboost: A scalable tree boosting system。The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,(會議日期: 13-17 August, 2016)。ACM。785-794。
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Simonyan, Karen、Zisserman, Andrew(2015)。Very deep convolutional networks for large-scale image recognition。The 3rd International Conference on Learning Representations。Computing Research Repository。1-14。
3.
Chu, W. T.、Situmeang, S.(2017)。Badminton video analysis based on spatiotemporal and stroke features。The 2017 ACM on International Conference on Multimedia Retrieval。Bucharest。
4.
Cao, Z.、Hidalgo, G.、Simon, T.、Wei, S. E.、Sheikh, Y.(2017)。OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields。2017 IEEE Conference on Computer Vision and Pattern Recognition。Honolulu, Hawaii。1302-1310。
5.
Huang, Yu-Chuan、Liao, I-No、Chen, Ching-Hsuan、İk, Tsì-Uí、Peng, Wen-Chih(2019)。TrackNet: A deep learning network for tracking high-speed and tiny objects in sports applications。The 2019 16th IEEE International Workshop of Content-Aware Video Analysis (CAVA-AVSS19) in conjunction with the 16th IEEE International Conference on Advanced Video and Signal-based Surveillance,Institute of Electrical and Electronics Engineers (會議日期: 2019/09/18-09/21)。
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Farin, D.、Krabbe, S.、Effelsberg, W.、de With, P. H. N.(2004)。Robust camera calibration for sport videos using court models。SPIE Proceedings,80-91。
7.
Redmon, J.、Farhadi, A.(2017)。YOLO9000: Better, faster, stronger。2017 IEEE Conference on Computer Vision and Pattern Recognition。
8.
Peters, M. E.、Neumann, M.、Iyyer, M.、Gardner, M.、Clark, C.、Lee, K.、Zettlemoyer, L.(2018)。Deep contextualized word representations。New Orleans, LA。2227-2237。
9.
Noh, Hyeonwoo、Hong, Seunghoon、Han, Bohyung(2015)。Learning deconvolution network for semantic segmentation。The 2015 IEEE International Conference on Computer Vision,Institute of Electrical and Electronics Engineers 1520-1528。
10.
Supriyanto, N. A.、Rasyid, A.(2018)。Characteristics analysis of badminton in female single player。The 1st Conference on Interdisciplinary Approach in Sports。Yogyakarta。
11.
Redmon, Joseph、Divvala, Santosh、Girshick, Ross、Farhadi, Ali(2016)。You only look once: Unified, real-time object detection。The 29th IEEE Conference on Computer Vision and Pattern Recognition,(會議日期: 27-30 June 2016),779-788。
單篇論文
1.
Redmon, J.,Farhadi, A.(2018)。YOLOv3: An incremental improvement,https://arxiv.org/abs/1804.02767,(1804.02767)。
其他
1.
金茂勛(20181217)。羽球》遭石宇奇直落二橫掃桃田賢斗:我被研究透徹了,https://www.storm.mg/。
延伸查詢
2.
Lee, C. W.,Ko, L.(20180803)。Taiwanese world No. 1 loses in BWF World Championships quarterfinals,https://focustaiwan.tw。
3.
Scott, H.(20140911)。Shuttlecock and balls: The fastest moving objects in sport,http://www.independentsportsnews.com。
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