Computational Intelligence in Sports: A Systematic Literature Review

Bonidia, Robson Parmezan; Rodrigues, Luiz; Avila-Santos, Anderson P.; Sanches, Danilo Sipoli; Brancher, Jacques Duílio

doi:10.1155/2018/3426178

Cited by 19 publications

(9 citation statements)

References 65 publications

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“…D. Tan et al [176] review video-based action recognition approaches in badminton, such as recognizing the actions of service and smashing, while team sports and other individual sports are not considered and the popular datasets used for action recognition are not introduced. Although J. Gudmundsson et al [177], R. Bonidia et al [178] and R. Beal et al [179] review multiple sports, they pay much attention on sports data mining instead of video action recognition. M. Manafifard et al [180] propose a survey on player tracking in soccer videos, which also reviews video technologies like object tracking and detection, however, only soccer is taken into account.…”

Section: Sportmentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

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Section: Sportmentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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“…The real-time individualized core body temperature estimate is provided by the model parameters. In this paper, author details the contemporary statistical approaches from Machine Learning and Data Mining environments for robust predictive models building [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

Wang

Guo

2021

IJCAI

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With the continuous development of track and field sports, a relatively complete and scientific system has been formed in terms of training, and more attention has been paid to the planned training of teenagers for many years, psychological training and recovery training, and strengthening medical supervision and scientific research. In order to speed up the scientific training of track and field and improve the level of track and field training in China, in view of the characteristics of track and field injuries such as multiple causes and complexity, this paper analyzes and studies the attribute reduction algorithm based on attribute reduction algorithm, and proposes a sports injury early warning model based on mutual information. Taking the reduction results as input neurons, a BP neural network with hidden layer is established in MATLAB environment. The results show that: call 85% of the track and field athletes in the sample information as training samples for training. After the simulation experiment of the model, it is found that the error value of the prediction results is basically controlled in the range of-0.025-0.05, which meets the requirements of early warning accuracy of injury risk level of athletes, and the accuracy rate of early warning of sports injury risk reaches 100%.Povzetek: Razvit je sistem za zgodnje napovedovanje športnih poškodb.

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“…With the amount of information and data in the sport domain, opportunities for data mining and machine learning applications can be extremely widespread, and benefits from the results can be enormous (Bonidia, Rodriges, Avila-Santos, A.P., Sanches, & Brancher, 2018;Horvat & Josip, 2020;Li & Zhang, 2012;Ofoghi, Zeleznikow, MacMahon, & Raab, 2013;Schumaker, Soleiman, & Chen, 2010). The evolution of tracking systems has become another opportunity for researchers in the sport domain to extract new knowledge relate to player performance, decisionmaking, and movement patterns, among others (Biermann et al, 2021;Jamil et al, 2021;Raabe et al, 2022;Rein & Memmert, 2016;Stein et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Time Series Data Mining for Sport Data: a Review

Komitova

Raabe

Rein

et al. 2022

International Journal of Computer Science in Sport

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Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.

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Computational Intelligence in Sports: A Systematic Literature Review

Cited by 19 publications

References 65 publications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

Time Series Data Mining for Sport Data: a Review

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