Recognizing Events in Spatiotemporal Soccer Data

Khaustov, Victor; Mozgovoy, Maxim

doi:10.3390/app10228046

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…Nowadays, deep learning-based methods are used in a wide variety of soccer-related computer vision tasks, such as action spotting 4 – 10 , player segmentation 11 , counting 12 and tracking 13 , 14 , ball tracking 15 , tactics analysis 16 , pass feasibility 17 , talent scouting 18 , game phase analysis 19 , or highlights generation 20 , 21 . To achieve such objectives, the methods developed often rely on either generic learning-based algorithms 22 , 23 trained on public datasets 24 , 25 , and/or on fine-tuned algorithms trained on private task-specific soccer datasets 7 , 9 , 19 . In the first case, the performances achieved are suboptimal, while in the second case, they cannot be reproduced by a third party.…”

Section: Background and Summarymentioning

confidence: 99%

Scaling up SoccerNet with multi-view spatial localization and re-identification

et al. 2022

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Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet’s broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations.

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Section: Background and Summarymentioning

confidence: 99%

Scaling up SoccerNet with multi-view spatial localization and re-identification

et al. 2022

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“…These systems are able to evaluate the activities of players successfully [79] such as the distance covered by players, shot detection [80,81], the number of sprints, player's position, and their movements [82,83], the player's relative position concerning other players, possession [84] of the soccer ball and motion/gesture recognition of the referee [85], predicting player trajectories for shot situations [86]. The generated data can be used to evaluate individual player performance, occlusion handling [21] by the detecting position of the player [87], action recognition [88], predicting and classifying the passes [89][90][91], key event extraction [92][93][94][95][96][97][98][99][100][101], tactical performance of the team [102][103][104][105][106], and analyzing the team's tactics based on the team formation [107][108][109], along with generating highlights [110][111][112][113]. Table 3 summarizes various proposed methodologies to resolve various challenging tasks in soccer with their limitations.…”

Section: Soccermentioning

confidence: 99%

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

2022

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Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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“…Besides those works, the literature is rich in papers using either small custom datasets, such as [16,23], or focusing on event recognition from pre-cut clips and selected frames rather than spotting actions in untrimmed videos, such as [27,28,29], or even targeting a single class, such as goals [45]. In this work, we tackle the large-scale action spotting task of SoccerNet-v2, the extension of SoccerNet proposed by Deliège et al [13].…”

Section: Related Workmentioning

confidence: 99%

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Cioppa¹,

Deliège²,

Magera³

et al. 2021

Preprint

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Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the largescale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances.

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Recognizing Events in Spatiotemporal Soccer Data

Cited by 17 publications

References 18 publications

Scaling up SoccerNet with multi-view spatial localization and re-identification

Scaling up SoccerNet with multi-view spatial localization and re-identification

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

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