Soccer Event Detection

Khan, Abdullah; Lazzerini, Beatrice; Calabrese, Gaetano; Serafini, Luciano

doi:10.5121/csit.2018.80509

Cited by 25 publications

(16 citation statements)

References 16 publications

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“…For example, the English Premier League compiles and publishes basic statistical facts about their soccer players, and, in general, such data is often reported in newspapers and various "yearbooks". Some research works are focused on analyzing soccer video streams and detecting the events directly from them [8,9]. However, video recordings of complete matches of a particular event might be hard to obtain, and their distribution is typically strictly controlled by the copyright holder.…”

Section: Background and Related Workmentioning

confidence: 99%

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: 99%

Recognizing Events in Spatiotemporal Soccer Data

Khaustov

Mozgovoy

2020

Applied Sciences

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“…Moreover, it could be useful to have a record of the ball position to improve our analysis. In this context, it becomes relevant the use of alternatives measurement techniques base on artificial intelligence and visual recognition [40][41][42].…”

Section: Discussionmentioning

confidence: 99%

Stochastic model for football's collective dynamics

Chacoma,

Almeira,

Perotti

et al. 2021

Preprint

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In this paper, we study collective interaction dynamics emerging in the game of football-soccer.To do so, we surveyed a database containing body-sensors traces measured during three professional football matches, where we observed statistical patterns that we used to propose a stochastic model for the players' motion in the field. The model, which is based on linear interactions, captures in good approximation the spatiotemporal dynamics of a football team. Our theoretical framework, therefore, becomes an effective analytical tool to uncover the underlying cooperative mechanisms behind the complexity of football plays. Moreover, we showed that it can provide handy theoretical support for coaches to evaluate teams' and players' performances in competitive scenarios. I. INTRODUCTIONThe use of complex systems theory as an alternative paradigm for analyzing elite sport dynamics is currently arousing intense academic interest [1][2][3][4][5]. Fostered by the new advances in data acquisition [6,7] and artificial intelligence techniques [8,9], the use of state-of-theart statistical tools to evaluate teams' performances is, nowadays, shaping a new profile of data-driven-based professional coaches worldwide.Formally, sports teams can be thought of as complex sociotechnical systems [10], where a wide range of organizational factors might interact to influence athletes' performances [11][12][13][14]. Particularly, in collective games like football, cooperative interplay dynamics seem to be a key feature to be analyzed [15,16]. In principle, collective behaviors in soccer are important since they are connected to team tactics and strategies. Usually, features of these collective behaviors are described by using simple group-level metrics [17][18][19][20][21][22]. Furthermore, temporal sequences of ball and player movements in football, showing traits of complex behaviors, have been reported and studied using stochastic models and statistical analysis [23][24][25][26].Recent works has focused on describing cooperative on-ball interaction in football within the framework of network science [27][28][29][30][31]. In [32], for instance, D. Garrido et al. studied the so call Pitch Passing Networks in the games of the Spanish League at 2018/2019 season.

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“…Earlier approaches for the automated detection of selected events have employed probabilistic models such as Bayesian Networks [48,49], and hidden Markov model (HMM) [50][51][52][53][54]. With the rise of machine learning, there has been growing interest in using support vector machine (SVM) [55][56][57][58][59][60] and deep learning [45,[61][62][63][64][65] due to their relatively higher performance (detection accuracy) and the availability of more advanced computing infrastructures.…”

Section: Event Detection In Soccer Videosmentioning

confidence: 99%

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Rongved

Stige

Hicks

et al. 2021

MAKE

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Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.

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Soccer Event Detection

Abstract: ABSTRACT

Cited by 25 publications

References 16 publications

Recognizing Events in Spatiotemporal Soccer Data

Recognizing Events in Spatiotemporal Soccer Data

Stochastic model for football's collective dynamics

Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

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