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Applied Sciences
Volume 14
Issue 11
10.3390/app14114847
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Open AccessArticle
by Ryota Goka SciProfilesScilitPreprints.orgGoogle Scholar Yuya Moroto SciProfilesScilitPreprints.orgGoogle Scholar Keisuke Maeda SciProfilesScilitPreprints.orgGoogle Scholar Takahiro Ogawa SciProfilesScilitPreprints.orgGoogle Scholar Miki Haseyama SciProfilesScilitPreprints.orgGoogle ScholarRyota Goka
,
Yuya Moroto
,
Keisuke Maeda
,
Takahiro Ogawa
and
Miki Haseyama
1
Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan
2
Data-Driven Interdisciplinary Research Emergence Department, Hokkaido University, N-13, W-10, Kita-ku, Sapporo 060-0813, Japan
3
Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(11), 4847; https://doi.org/10.3390/app14114847
Submission received: 6 April 2024/Revised: 24 May 2024/Accepted: 27 May 2024/Published: 3 June 2024
(This article belongs to the Collection Computer Science in Sport)
Abstract
Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods.
Keywords: event prediction; deep learning; sports video analysis; multimodal machine learning
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MDPI and ACS Style
Goka, R.; Moroto, Y.; Maeda, K.; Ogawa, T.; Haseyama, M.Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos. Appl. Sci. 2024, 14, 4847.https://doi.org/10.3390/app14114847
AMA Style
Goka R, Moroto Y, Maeda K, Ogawa T, Haseyama M.Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos. Applied Sciences. 2024; 14(11):4847.https://doi.org/10.3390/app14114847
Chicago/Turabian Style
Goka, Ryota, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama.2024. "Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos" Applied Sciences 14, no. 11: 4847.https://doi.org/10.3390/app14114847
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Goka, R.; Moroto, Y.; Maeda, K.; Ogawa, T.; Haseyama, M.Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos. Appl. Sci. 2024, 14, 4847.https://doi.org/10.3390/app14114847
AMA Style
Goka R, Moroto Y, Maeda K, Ogawa T, Haseyama M.Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos. Applied Sciences. 2024; 14(11):4847.https://doi.org/10.3390/app14114847
Chicago/Turabian Style
Goka, Ryota, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama.2024. "Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos" Applied Sciences 14, no. 11: 4847.https://doi.org/10.3390/app14114847
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Appl. Sci.,EISSN 2076-3417,Published by MDPI
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