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Deep Learning for Domain-Specific Action Recognition in Tennis
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In order for action recognition to be useful in sports analytics a finer-grained action classification is needed. ...
Recent progress in sports analytics has been driven by the availability of spatio-temporal and high level data. ...
With this work we wish to motivate the exploration of deep neural networks in the sports domain and the use and production of benchmark datasets in sports action recognition. ...
doi:10.1109/cvprw.2017.27
dblp:conf/cvpr/MoraK17
fatcat:n67b5hpxgzdgrehq7ffjjwpiam
Machine Learning on Spatiotemporal Data in Football: A Survey of Methods and Problems
[chapter]
2022
DAAAM Proceedings
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Machine learning is growing exponentially, and its applications are gaining more traction in the sports analysis community in recent years. ...
The application of machine learning methods on spatiotemporal data in sports like football is getting attention from football clubs, academics, and amateur analysts and is the focus of this survey. ...
Conclusion This review aimed to evaluate the current state of machine learning in sports analytics, particularly interested in applications on data analytics in football. ...
doi:10.2507/33rd.daaam.proceedings.070
fatcat:i6pca6pcxbhqbb5xsphvnyr6ky
Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts
2022
IEEE Access
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In this study, we propose a new evaluation method for racket sports based on deep reinforcement learning, which can analyze the motion of a player in more detail, rather than only considering the results ...
Evaluating the performance of players in a dynamic competition is vital for achieving effective sports coaching. ...
INTRODUCTION With the development of deep learning technologies, computer vision-based deep learning methods have become increasingly important in sports analytics. ...
doi:10.1109/access.2022.3175314
fatcat:zkapys4r2rhtxe232sl5if2cqa
Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning
2021
Security and Communication Networks
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This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm ...
With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. ...
studying deep learning is to establish a neural network that simulates the human brain for analytical learning. ...
doi:10.1155/2021/6924262
fatcat:wfdpgdrlbvg7fpipym4pfrpelm
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL ...
A variety of machine learning models have been proposed to assess the performance of players in professional sports. ...
Conclusion and Future Work We investigated Deep Reinforcement Learning (DRL) for professional sports analytics. We applied DRL to learn complex spatio-temporal NHL dynamics. ...
doi:10.24963/ijcai.2018/478
dblp:conf/ijcai/LiuS18
fatcat:wo2nqfztp5a7lcroa7cc6psbly
Session-Based Path Prediction by Combining Local and Global Content Preferences
[chapter]
2020
Lecture Notes in Computer Science
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We present DRS-LaG, a Deep Reinforcement Learning System, based on Local and Global preferences. We capture these global content preferences by tracking a key analytics KPI, the number of views. ...
While webpages visited by the user in the current session capture the users' local preferences, in this work, we show how the global content preferences at the given instant can assist in this task. ...
The goal is to learn a policy π : S → A to maximize the cumulative reward of the system. To deal with the dynamic action spaces, we use a Deep Q-Learning model-free approach. ...
doi:10.1007/978-3-030-45442-5_16
fatcat:doynunfsxvdh3obmbjuj3fou3a
Detection of Problem Gambling with Less Features Using Machine Learning Methods
[article]
2024
arXiv
pre-print
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Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions. ...
In this study, we propose a deep neural networks PGN4 that performs well when using limited analytic features. ...
Both datasets include multiple modalities of online gambling such as live action sports gambling, fix-odds sporting betting, casino, poker, and games like backgammon. ...
arXiv:2403.15962v1
fatcat:snicl2xeojfthfvzns64lsbhti
Wage against the machine: A generalized deep-learning market test of dataset value
2017
International Journal of Forecasting
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It relies on the use of deep learning, comprehensive historical box score statistics, and the existence of betting markets. ...
How can you tell whether a particular sports dataset really adds value, particularly with regard to betting effectiveness? ...
Acknowledgments I am grateful for the contributions of each member of the Vantage Sports team, especially Brett McDonald, Chase Exon, Cameron Tangney, Mark Jansen, Scott Snider, and Brandon Sedgwick, and ...
doi:10.1016/j.ijforecast.2017.09.008
fatcat:vtycmgpkj5gl7efqtrnsszg3ju
Application of Distributed Probability Model in Sports Based on Deep Learning: Deep Belief Network (DL-DBN) Algorithm for Human Behaviour Analysis
2022
Computational Intelligence and Neuroscience
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In this research, the Deep Learning-Deep Belief Network (DL-DBN) algorithm is implemented with probability to analyse human behaviour in sports and implement a distributed probability model for classifying ...
It is significant to determine human behaviour analysis in the context of sports. ...
on the human behavior in sports with the aid of Deep Learning technology and probability statistics is implemented and the results were discussed. ...
doi:10.1155/2022/7988844
pmid:35222635
pmcid:PMC8881180
fatcat:koarffcb4fgrnprwdpgci54uiy
Optimizing the best play in basketball using deep learning
2022
Journal of Sports Analytics
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A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. ...
It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful. ...
Introduction The increasing interest in sports analytics over the last two decades can be attributed to advances in technology where data has been used by teams and individuals to gain a competitive advantage ...
doi:10.3233/jsa-200524
fatcat:4tqquqhra5ffpedh6edvcqdcby
Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists
2021
Frontiers in Sports and Active Living
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Likewise, for many, the motivations for adopting a machine learning (ML) paradigm in sports analytics are still either faint or unclear. ...
In this perspective paper, we present a high-level, non-technical, overview of the machine learning paradigm that motivates its potential for enhancing sports (performance and business) analytics. ...
Indeed, as we discuss later, multi-layer ANNs, now commonly referred to as Deep Learning, have become one of the most popular techniques in sports related analytics. ...
doi:10.3389/fspor.2021.682287
pmid:34957395
pmcid:PMC8692708
fatcat:fpzi4foulbcbbpculx752eeuzy
Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks
2022
Computational Intelligence and Neuroscience
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The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students' learning and understanding of actions in ...
The network improves the efficiency of the whole action video learning. ...
Action Recognition
Conclusion Currently, video analytics and the entire technology stack of big data systems rely on deep learning. ...
doi:10.1155/2022/7835241
pmid:35958770
pmcid:PMC9357763
fatcat:xzww4oko7jcvhdo6u2fhirdamq
Analyzing Basketball Movements and Pass Relationships using Realtime Object Tracking Techniques based on Deep Learning
2019
IEEE Access
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INDEX TERMS Sports analytics, object detection, complex networks, deep learning, video processing. 56564 2169-3536 ...
We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts. ...
EVALUATION Our evaluation was conducted on a deep learning machine equipped with Intel R Core I7-8700 CPU, Nvidia GeForce GTX 1080Ti GPU, and 32GB of memory. ...
doi:10.1109/access.2019.2913953
fatcat:blouqn6g2rgjpbqknqmqhnc3mu
Single Run Action Detector over Video Stream – A Privacy Preserving Approach
[article]
2021
arXiv
pre-print
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It is based on Faster-RCNN combined with temporal shift modeling and segment based sampling to capture the human actions. ...
Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on ...
The recent approaches in video analytic and deep learning algorithms like Convolutional Neural network provides the opportunity for real-time detection and analysis of human behaviors like walking,running ...
arXiv:2102.03391v1
fatcat:oxeioiezqnbjpnpoenelcsnbhy
Towards Structured Analysis of Broadcast Badminton Videos
[article]
2017
arXiv
pre-print
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Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. ...
In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. ...
For players detection, we rely on robust deep learning detection techniques. ...
arXiv:1712.08714v1
fatcat:jpwbz6o2pjazzaq2ju7wm3izde
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