ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis
Authors: 
Ting-Yang Kao, Tse-Yu Pan, Chen-Ni Chen, Tsung-Hsun Tsai, Hung-Kuo Chu, Min-Chun HuAuthors Info & Claims
Ting-Yang Kao, Tse-Yu Pan, Chen-Ni Chen, Tsung-Hsun Tsai, Hung-Kuo Chu, Min-Chun HuAuthors Info & ClaimsPages 6991 - 6993
Abstract
We propose a fine-grained basketball shot analysis system called ScoreActuary to analyze the players' shot events, which can be applied to game analysis, player training, and highlight generation. Given a basketball video as input, our system first detects/segments shot candidates and then analyzes "Shot Type", "Shot Result", and "Ball Status" of each shot candidate in real-time. Our approach is composed of a customized object detector and a trajectory-aware network to learn the information of ball trajectory. Compared to the existing methods that analyze basketball shots, our algorithm can better handle videos with arbitrary camera movements while improving the accuracy. To the best of our knowledge, this work is the first system that can analyze fine-grained shot events accurately in real basketball games with arbitrary camera movements.
References
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Xu-Bo Fu, Shao-Long Yue, and De-Yun Pan. 2021. Camera-based basketball scoring detection using convolutional neural network. International Journal of Automation and Computing 18, 2 (2021), 266--276.
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Wu Liu, Chenggang Clarence Yan, Jiangyu Liu, and Huadong Ma. 2017. Deep learning based basketball video analysis for intelligent arena application. Multimedia Tools and Applications 76, 23 (2017), 24983--25001.
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Nex Team Inc. 2017. HomeCourt. Retrieved May 11, 2022 from https://www.homecourt.ai/
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Osman Murat Teket and Imam Samil Yetik. 2020. A Fast Deep Learning Based Approach for Basketball Video Analysis. In Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing. 1--6.
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Index Terms
- ScoreActuary: Hoop-Centric Trajectory-Aware Network for Fine-Grained Basketball Shot Analysis
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Information & Contributors
Information
Published In
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
- General Chairs:
- João Magalhães,
- Alberto del Bimbo,
- Shin'ichi Satoh,
- Nicu Sebe,
- Program Chairs:
- Xavier Alameda-Pineda,
- Qin Jin,
- Vincent Oria,
- Laura Toni
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Publication History
Published: 10 October 2022
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- Demonstration
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- Ministry of Science and Technology
Conference
MM '22
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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
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