Slicing and Dicing Soccer: Automatic Detection of Complex Events from Spatio-Temporal Data

Morra, Lia; Manigrasso, Francesco; Canto, Giuseppe; Gianfrate, Claudio; Guarino, Enrico; Lamberti, Fabrizio

doi:10.1007/978-3-030-50347-5_11

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…Event recognition in player tracking data is the subject of several research works. The best results to date have been reported in Richly et al [22] and Morra et al [23]. However, it seems that obtaining the most accurate results was not the main goal of these works.…”

Section: Discussionmentioning

confidence: 99%

“…However, it seems that obtaining the most accurate results was not the main goal of these works. Both papers were focused on the evaluation of specific methods as seen from the conclusions made by their respective authors: "the results showed that neural networks present a viable model to detect events in soccer data" [22]; "we have shown that ITLs (interval temporal logics) are capable of accurately detecting most events from positional data" [23]. Therefore, high accuracy demonstrates the versatility of the suggested methods and provides a firm ground for their use in similar tasks.…”

Section: Discussionmentioning

confidence: 99%

“…As noted by Morra et al [23], it is difficult to compare results obtained in different research works due to differences in experimental settings, types of events, and used datasets. Therefore, details of the experimental setup are essential, as they might have a significant impact on results.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, these works are dedicated either to categorizing and labeling known events [18,19] or predicting future events [20,21]. Certain approaches to automatic event detection have been proposed only in more recent papers [16,22,23].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Morra et al [23] experimented with a much larger Soccer Event Recognition (SoccER) dataset comprising 500 min of gameplay. This dataset consists of artificial (simulated) soccer matches obtained with an open-source Gameplay Football engine [24].…”

Section: Background and Related Workmentioning

confidence: 99%

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Recognizing Events in Spatiotemporal Soccer Data

Khaustov

Mozgovoy

2020

Applied Sciences

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Spatiotemporal datasets based on player tracking are widely used in sports analytics research. Common research tasks often require the analysis of game events, such as passes, fouls, tackles, and shots on goal. However, spatiotemporal datasets usually do not include event information, which means it has to be reconstructed automatically. We propose a rule-based algorithm for identifying several basic types of events in soccer, including ball possession, successful and unsuccessful passes, and shots on goal. Our aim is to provide a simple procedure that can be used for practical soccer data analysis tasks, and also serve as a baseline model for algorithms based on more advanced approaches. The resulting algorithm is fast, easy to implement, achieves high accuracy on the datasets available to us, and can be used in similar scenarios without modification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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Recognizing Events in Spatiotemporal Soccer Data

Khaustov

Mozgovoy

2020

Applied Sciences

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Prior Knowledge Base

Tao,

Duan,

Qin

et al. 2024

Wireless Networks

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PlayNet: real-time handball play classification with Kalman embeddings and neural networks

Mures,

Taibo,

Padrón

et al. 2023

Vis Comput

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Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification. Our method is based on Kalman embeddings, a new low-dimensional representation for game states that enables efficient operation on commodity hardware and customized camera layouts. Firstly, we leverage Kalman filtering to detect and track the main agents in the playing field, allowing us to represent them in a single normalized coordinate space. Secondly, we utilize a neural network trained in nonlinear dimensionality reduction through fuzzy topological data structure analysis. As a result, PlayNet achieves real-time play classification with under 55 ms of latency on commodity hardware, making it a promising addition to automated live broadcasting and game analysis pipelines.

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Slicing and Dicing Soccer: Automatic Detection of Complex Events from Spatio-Temporal Data

Cited by 10 publications

References 13 publications

Recognizing Events in Spatiotemporal Soccer Data

Recognizing Events in Spatiotemporal Soccer Data

Prior Knowledge Base

PlayNet: real-time handball play classification with Kalman embeddings and neural networks

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