BallTrack: Football ball tracking for real-time CCTV systems

Komorowski, Jacek; Kurzejamski, Grzegorz; Sarwas, Grzegorz

doi:10.23919/mva.2019.8757880

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In such a case, a jersey number must be detected to recognize the player [60]. Accurate tracking [61][62][63][64][65][66][67][68][69][70][71][72] by detection [73][74][75][76] of multiple soccer players as well as the ball in real-time is a major challenge to evaluate the performance of the players, to find their relative positions at regular intervals, and to link spatiotemporal data to extract trajectories. The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye.…”

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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Section: Soccermentioning

confidence: 99%

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

2022

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“…When they use a buffer size of 50 frames, the detection rate is 68.5% only. A deep-learning-based system is also proposed in [18] and [19] for CCTV footage videos. Leo et al [20] present a multi-step algorithm to detect the ball in image sequences acquired from fixed cameras.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

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Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.

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“…Cascade thresholds are found using grid search. Advancements have also been done on ball detection with DeepBall, Komorowski et al, 2019 [27] and [26] where a deep network based detector is presented. It specializes in ball detection in long shot videos by means of using hypercolumn concept, where feature maps from different hierarchy levels of the deep convo lutional network are combined and jointly fed to the convolutional classification layer.…”

Section: Existing Workmentioning

confidence: 99%

Amateur football analytics using computer vision

Mavrogiannis

2022

Neural Comput & Applic

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BallTrack: Football ball tracking for real-time CCTV systems

Cited by 6 publications

References 12 publications

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

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

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Amateur football analytics using computer vision

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