题名

影像動作辨識系統用於區分籃球動作技能與輔助判定爭議球的效益:系統性回顧

并列篇名

Effect of video action recognition system on differentiating basketball motor skills and assisting in determination of disputed balls: A systematic review

DOI

10.6223/qcpe.202309_37(3).0002

作者

陳羿揚(Yi-Yang Chen);黃筱祺(Xiao-Qi Huang)

关键词

卷神經網路 ; 穿戴裝置 ; 運動學 ; 情蒐 ; 自動化 ; Convolutional Neural Network ; wearable device ; kinematics ; information collection ; automation

期刊名称

中華體育季刊

卷期/出版年月

37卷3期(2023 / 09 / 01)

页次

223 - 240

内容语文

繁體中文;英文

中文摘要

早期籃球技能訓練與爭議球判定皆以教練與裁判主觀經驗為依據,如今影像動作辨識系統與穿戴裝置,陸續被提倡用於量化動作姿態、辨別專項技能優劣與輔助吹判,期望能更客觀提升整體產業價值與吹判公正性,然而動作辨識系統如何應用於辨識籃球動作技能與輔助判定爭議球,仍缺乏有系統的彙整近年相關實證研究釐清具體效益。本研究目的為透過系統性回顧釐清2019年1月至2022年8月,共3年7個月間,影像動作辨識系統用於區分籃球動作技能與輔助判定爭議球的研究現況。並且解析此類主題適用的對象(參與者)、使用數據資料庫、觀測哪些事件、影片分辨率與幀數、影片事件數量與準確度為何。接續提出此類主題未來研究方向與相關建議,供相關研究員與賽事從業人員,深入瞭解動作辨識系統應用於籃球訓練與輔助吹判的具體效益。

英文摘要

Early basketball training and disputed ball judgments mostly were based on the personal experience of coaches and referees. Nowadays, video action recognition systems and wearable devices have been successively promoted to quantify movement postures, distinguishing specialized skill levels, and assist in adjudication. We expecting the value of the technology industry would be increased objectively as well as the fairness of the judgment. However, there is still a lack of systematic review regarding the application and feasibility of how video action recognition systems could be used to identify basketball movement skills and assist in determination of disputed balls in recent empirical studies. This study systematically reviewed empirical and reviewed articles written in Chinese and English ranged from January, 2019 to August, 2022. The follow-up discussion was based on the deep learning models, classification strategies, participants observed, the use of a database, what type of events being observed, video resolution and frame rate, and what is the number of video events and how accurate are they? The results provided an insight of the effectiveness of video action recognition systems in basketball training and referee assisting for researchers and competition officials.

主题分类 社會科學 > 體育學
参考文献
  1. 方麒堯, C.-Y.,陳韋翰, W.-H.,相子元, T.-Y.(2021)。運動軌跡追蹤系統之發展與回顧。中華體育季刊,35(2),125-136。
    連結:
  2. 周育晨, Y.-C.,李恆儒, H.-J.(2020)。以穿戴式裝置探討不同專項位置籃球員與訓練情境之運動負荷。體育學報,53(3),315-326。
    連結:
  3. 張簡旭芳, H.-F.,李尹鑫, Y.-S.,相子元, T.-Y.(2016)。穿戴科技於運動科學之應用。中華體育季刊,30(2),121-127。
    連結:
  4. 陳羿揚, Y.-Y.,林琨瀚, K.-H.,邱文信, K.-H.(2021)。手指動作對改變棒球投擲球路的效益:系統性回顧。中華體育季刊,35(2),113-124。
    連結:
  5. 陳羿揚, Y.-Y.,莎麗娃, S.,邱文信, W.-H.(2020)。三軸加速規用於評估身體活動量的方法與應用。華人運動生物力學期刊,17(1),45-53。
    連結:
  6. 曾國棟, K.-T.,劉有德, Y.-T.(2010)。高中籃球聯賽攻守紀錄主成分分析。大專體育學刊,12(2),43-50。
    連結:
  7. Bouwmans, T.,Javed, S.,Sultana, M.,Jung, S. K.(2019).Deep neural network concepts for background subtraction: A systematic review and comparative evaluation.Neural Networks,117,8-66.
  8. Cheng, Y.,Liang, X.,Xu, Y.,Kuang, X.(2022).Artificial intelligence technology in basketball training action recognition.Frontiers in Neurorobotics,16,1-18.
  9. Clark, H. D.,Wells, G. A.,Huët, C.,McAlister, F. A.,Salmi, L. R.,Fergusson, D.,Laupacis, A.(1999).Assessing the quality of randomized trials: Reliability of the Jadad scale.Controlled Clinical Trials,20(5),448-452.
  10. Deng, Q.,Zhou, W.,Reynoso, L. C.(2022).Decision support model for student physical exercise health promotion based on artificial neural network.Archives of Clinical Psychiatry,49(1),36-43.
  11. Du, Y.,Zhao, Q.,Lu, X.(2021).Semantic extraction of basketball game video combining domain knowledge and in-depth features.Scientific Programming,2021,2-10.
  12. Fan, J.,Bi, S.,Wang, G.,Zhang, L.,Sun, S.(2021).Sensor fusion basketball shooting posture recognition system based on CNN.Journal of Sensors,2021,1-16.
  13. Fan, J.,Bi, S.,Xu, R.,Wang, L.,Zhang, L.(2021).Hybrid lightweight deep-learning model for sensor-fusion basketball shooting-posture recognition.Measurement,189,110595.
  14. Gao, Z.,Xuan, H. Z.,Zhang, H.,Wan, S.,Choo, K. K. R.(2019).Adaptive fusion and category-level dictionary learning model for multiview human action recognition.IEEE Internet of Things Journal,6(6),9280-9293.
  15. Gu, J.,Wang, Z.,Kuen, J.,Ma, L.,Shahroudy, A.,Shuai, B.,Liu, T.,Wang, X.,Wang, G.,Cai, J.,Chen, T.(2018).Recent advances in convolutional neural networks.Pattern Recognition,77,354-377.
  16. Hong, X.(2021).Basketball data analysis using spark framework and k-means algorithm.Journal of Healthcare Engineering,2021,2-7.
  17. Hu, X.,Mo, S.,Qu, X.(2020).Basketball activity classification based on upper body kinematics and dynamic time warping.International Journal of Sports Medicine,41(4),255-263.
  18. Hua, L.,Liu, G.(2021).Development of basketball tactics basic cooperation teaching system based on CNN and BP neural network.Computational Intelligence and Neuroscience,2021,2-12.
  19. Jadad, A. R.,Moore, R. A.,Carroll, D.,Jenkinson, C.,Reynolds, D. J. M.,Gavaghan, D. J.,McQuay, H. J.(1996).Assessing the quality of reports of randomized clinical trials: Is blinding necessary?.Controlled Clinical Trials,17(1),1-12.
  20. Jegham, I.,Khalifa, A. B.,Alouani, I.,Mahjoub, M. A.(2020).Soft spatial attention-based multimodal driver action recognition using deep learning.IEEE Sensors Journal,21(2),1918-1925.
  21. Khan, A. A.,Shao, J.,Ali, W.,Tumrani, S.(2020).Content-aware summarization of broadcast sports videos: An audio-visual feature extraction approach.Neural Processing Letters,52(3),1945-1968.
  22. Lee, H.,Song, J.(2019).Introduction to convolutional neural network using keras; an understanding from a statistician.Communications for Statistical Applications and Methods,26(6),591-610.
  23. Lian, C.,Ma, R.,Wang, X.,Zhao, Y.,Peng, H.,Yang, T.,Zhang, M.,Zhang, W.,Li, W. J.(2021).ANN enhanced IoT wristband for recognition of player identity, and shot types based on basketball shooting Motion Analysis.IEEE Sensors Journal,22(2),1404-1443.
  24. Ma, C.,Fan, J.,Yao, J.,Zhang, T.(2021).NPU RGBD dataset and a feature-enhanced LSTM-DGCN method for action recognition of basketball Players+.Applied Sciences,11(10),4426.
  25. Maddalena, L.,Petrosino, A.(2018).Background subtraction for moving object detection in RGBD data: A survey.Journal of Imaging,4(5),71.
  26. Majumder, S.,Kehtarnavaz, N.(2021).A review of real-time human action recognition involving vision sensing.Real-Time Image Processing and Deep Learning,2021,11736.
  27. Mangiarotti, M.,Ferrise, F.,Graziosi, S.,Tamburrino, F.,Bordegoni, M.(2019).A wearable device to detect in real-time bimanual gestures of basketball players during training sessions.Journal of Computing & Information Science in Engineering,19(1),1-10.
  28. Midha, S. S.,Rahela, S.(2018).Technological advancement in sports equipment.Indian Journal of Physical Education, Sports Medicine & Exercise Science,18(3),55-62.
  29. Min, E.,Guo, X.,Liu, Q.,Zhang, G.,Cui, J.,Long, J.(2018).A survey of clustering with deep learning: From the perspective of network architecture.IEEE Access,6,39501-39514.
  30. Nweke, H. F.,Teh, Y. W.,Al-Garadi, M. A.,Alo, U. R.(2018).Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges.Expert Systems with Applications,105,233-261.
  31. Olivo, S. A.,Macedo, L. G.,Gadotti, I. C.,Fuentes, J.,Stanton, T.,Magee, D. J.(2008).Scales to assess the quality of randomized controlled trials: A systematic review.Physical Therapy,88(2),156-175.
  32. Subha, I.,Narmadha, P.,Nivedha, S.,Sethukarasi, T.(2020).Real-time suspicious human action recognition from surveillance videos for resource-constrained devices.Journal of Computational and Theoretical Nanoscience,17(8),3790-3797.
  33. Suraj, K.,Wu, M. H.,Garud, I.,Sheng, Z.(2019).Automatic event detection in basketball using HMM with energy based defensive assignment.Journal of Quantitative Analysis in Sports,15(2),141-153.
  34. Thabtah, F.,Hammoud, S.,Kamalov, F.,Gonsalves, A.(2020).Data imbalance in classification: Experimental evaluation.Information Sciences,513,429-441.
  35. Wu, L.,Yang, Z.,Jian, M.,Shen, J.,Yang, Y.,Lang, X.(2021).Global motion estimation with iterative optimization-based independent univariate model for action recognition.Pattern Recognition,116,107925.
  36. Wu, L.,Yang, Z.,Wang, Q.,Jian, M.,Zhao, J.,Chen, C. W.(2020).Fusing motion patterns and key visual information for semantic event recognition in basketball videos.Neurocomputing,413(6),217-229.
  37. Yao, G.,Lei, T.,Zhong, J.(2019).A review of convolutional-neural-network-based action recognition.Pattern Recognition Letters,118,14-22.
  38. Yao, P.(2021).Real-time analysis of basketball sports data based on deep learning.Complexity,2021,2-11.
  39. Yuan, Y.,Lu, Z.,Yang, Z.,Jian, M.,Wu, L.,Li, Z.,Liu, X.(2022).Key frame extraction based on global motion statistics for team-sport videos.Multimedia Systems,28(2),387-401.
  40. Zhang, W.(2021).Research on recognition method of basketball goals based on image analysis of computer vision.Journal of Sensors,2021,2-11.
  41. Zhao, B.,Liu, S.(2021).Basketball shooting technology based on acceleration sensor fusion motion capture technology.EURASIP Journal on Advances in Signal Processing,2021(1),2-14.
  42. 國際籃球協會=International Basketball Federation(2022)。國際籃球協會 (2022)。FIBA 國際籃球規則即時重播系統 (IRS) 使用程序 V2.0。[International Basketball Federation. (2022). FIBA International basketball rules instant replay system (IRS) use program V2.0.]。
  43. 國際籃球協會=International Basketball Federation(2018)。國際籃球協會 (2018)。2018 國際籃球規則修改摘要。[International Basketball Federation. (2018). Rule Changes in official basketball rules 2018.]。
被引用次数
  1. (2024)。影像動作辨識系統用於羽球競賽對情蒐工作的效益:系統性回顧。中華體育季刊,38(2),131-150。