A multi-object tracking dataset for multiple sports and a study of cross-domain generality
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- SCOTT Atom James
- University of Tsukuba National Institute of Advanced Industrial Science and Technology
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- UCHIDA Ikuma
- University of Tsukuba National Institute of Advanced Industrial Science and Technology
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- DING Ning
- Nagoya University
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- RIKUHEI Umemoto
- Nagoya University
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- BUNKER Rory
- Nagoya University
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- KOBAYASHI Ren
- Nagoya University
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- KOYAMA Takeshi
- Tokai University
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- ONISHI Masaki
- National Institute of Advanced Industrial Science and Technology
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- KAMEDA Yoshinari
- University of Tsukuba
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- FUJII Keisuke
- Nagoya University
Bibliographic Information
- Other Title
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- 複数スポーツの多物体追跡データセットの構築と競技間における汎用性の検証
Abstract
<p>Tracking devices that can track players and balls are critical to the performance of sports teams. This paper presents a comprehensive multi-object tracking dataset for sports analysis. Our dataset comprises over 150 minutes of video footage from three sports: football, basketball, and handball. We captured complete pitch view footage using fisheye and drone cameras to enable holistic trajectory analysis. Additionally, we conduct ablation studies on trajectory prediction tasks to investigate the generalizability of learned features across different sports. Our dataset and findings provide valuable insights for sports analysis and can aid in developing advanced tracking and trajectory prediction algorithms.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2023 (0), 2H6OS8b04-2H6OS8b04, 2023
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390015333244481792
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed