Shoot Event Prediction from Soccer Videos by Considering Players’ Spatio-Temporal Relations

Goka, Ryota; Moroto, Yuya; Maeda, Keisuke; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/gcce56475.2022.10014347

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Thus, the BNN enables more reliable predictions than conventional methods based on deterministic machine learning. Note that we have previously presented the preliminary results in previous studies [38][39][40][41], where we demonstrated the effectiveness of incorporating visual information and players' distances into graphs to achieve shot prediction from soccer videos.…”

Section: Introductionmentioning

confidence: 74%

“…Furthermore, we adopted the following models for the comparison. To the best of our knowledge, our previous studies [38][39][40][41] are the only ones that use visual information to predict the shot event occurrence in soccer matches. Therefore, we employed DSA and DSTA as comparative methods although they are video-based traffic accident prediction models that consider spatial-temporal relationships between objects in the video.…”

Section: Settingsmentioning

confidence: 99%

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Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos

Goka,

Moroto,

Maeda

et al. 2024

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 74%

Section: Settingsmentioning

confidence: 99%

Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos

Goka,

Moroto,

Maeda

et al. 2024

Applied Sciences

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“…This refers to our previous approach [37], which is similar to the proposed method but uses a complete graph without any team information.…”

Section: As1mentioning

confidence: 99%

“…To our knowledge, our previous study [37] is the only method that predicts shoot event occurrence using visual information. The following additional methods were adopted as comparison methods, in addition to the comparative methods mentioned earlier.…”

Section: As3mentioning

confidence: 99%

“…The proposed method enables the prediction of the probability of shoot event occurrence and also allows for the calculation of the degree of uncertainty associated with making the prediction. Note that this study is an extension of our previous work [37], in which we proposed a more accurate prediction model by incorporating a complete bipartite graph with soccer-specific domain knowledge, including players' team information. The main contributions of this study are as follows.…”

Section: Introductionmentioning

confidence: 97%

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Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players’ Spatial-Temporal Relations

Goka

Moroto

Maeda

et al. 2023

Sensors

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In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players’ decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players’ spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players’ relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players’ distances significantly affects the prediction accuracy.

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Prediction of Shoot Events by Considering Spatio-temporal Relations of Multimodal Features

Goka,

Moroto,

Maeda

et al. 2023

2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)

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Shoot Event Prediction from Soccer Videos by Considering Players’ Spatio-Temporal Relations

Cited by 4 publications

References 5 publications

Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos

Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos

Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players’ Spatial-Temporal Relations

Prediction of Shoot Events by Considering Spatio-temporal Relations of Multimodal Features

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