Recognizing Compound Events in Spatio-Temporal Football Data

Richly, Keven; Rohloff, Tobias; Bothe, Max; Schwarz, Christian

doi:10.5220/0005877600270035

Cited by 11 publications

(7 citation statements)

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“…These rules were implemented using the ETALIS library for Prolog. The work of Morra et al dealt with a more complex set of events, so a direct comparison of the obtained results with the ones obtained by Richly et al [16,22] would be inaccurate. Still, the authors reported an improvement of precision at 96%, and to recall at 93%.…”

Section: Background and Related Workmentioning

confidence: 97%

“…On the other hand, ball-event datasets, such as the aforementioned Opta's F24 feed or the Soccer match event dataset [15] are typically focused on events, and do not provide complete player tracking data. In cases where manual event annotation is available, time synchronization might be inaccurate, and errors in player attribution are common [16].…”

Section: Background and Related Workmentioning

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%

“…Richly et al [16,22] applied several different machine learning-based methods to recognize four event types (pass, reception, clearance, and shot on goal) in a spatiotemporal soccer dataset. They prepared a "gold standard" manual markup comprising 194 events within 8:47 min of active game time and used it for training and testing different methods, including Support Vector Machine, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks.…”

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: Background and Related Workmentioning

confidence: 97%

Section: Background and Related Workmentioning

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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“…There are several recent studies aiming to detect basic events directly out of video footage (Ekin et al, 2003 ; Wickramaratna et al, 2005 ; Kolekar and Palaniappan, 2009 ) or positional data (Zheng and Kudenko, 2010 ; Motoi et al, 2012 ; Richly et al, 2016 ; Stein et al, 2019 ) and others focus on the identification of sophisticated tactical patterns (Hobbs et al, 2018 ; Andrienko et al, 2019 ; Shaw and Sudarshan, 2020 ; Anzer et al, 2021 ; Bauer and Anzer, 2021 ). The proposed approaches provide useful solutions for their respective tasks.…”

Section: Introductionmentioning

confidence: 99%

Toward Automatically Labeling Situations in Soccer

Fassmeyer

Anzer

Bauer

et al. 2021

Front. Sports Act. Living

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We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task.

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