Real-time multiview analysis of soccer matches for understanding interactions between ball and players

Leo, Marco; Mosca, Nicola; Spagnolo, Paolo; Mazzeo, Pier Luigi; D’Orazio, Tiziana; Distante, A.

doi:10.1145/1386352.1386419

Cited by 22 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collecting positional data is possible using modern image capture and processing technology [20,12]. There are commercial companies with similar systems such as Prozone [17] and Opta [16] which are used in professional leagues.…”

Section: Related Workmentioning

confidence: 99%

Classification of Passes in Football Matches Using Spatiotemporal Data

Chawla

Estephan

Gudmundsson

et al. 2017

ACM Trans. Spatial Algorithms Syst.

View full text Add to dashboard Cite

A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. We investigate the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. Furthermore, the learned classifiers are analysed to determine if there is a relationship between the complexity of the algorithm that computed the predictor variable and the importance of the variable to the classifier. Experimental results show that we are able to produce a classifier with 85.8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers. Finally, we show that the interrater agreement on pass classification between the machine classifier and a human observer is of similar magnitude to the agreement between two observers. Categories and Subject DescriptorsI.5 [Pattern Recognition]: Design Methodology-Feature evaluation and selection * A poster submission has also been made to the Large Scale Sports Analytics workshop at the KDD conference on this work. This submission covers the problem definition and solution framework described in this paper, and also includes preliminary experimental results on the accuracy of the framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Classification of Passes in Football Matches Using Spatiotemporal Data

Chawla

Estephan

Gudmundsson

et al. 2017

ACM Trans. Spatial Algorithms Syst.

View full text Add to dashboard Cite

show abstract

“…Many ball-tracking approaches for soccer [21,18], basketball [6], and volleyball [5, 10, 4] start with a set of suc-1 arXiv:1511.06181v2 [cs.CV] 1 Dec 2015…”

Section: Fitting Tracjectory Segmentsmentioning

confidence: 99%

What Players do with the Ball: A Physically Constrained Interaction Modeling

Maksai

Wang

Fua

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in lowresolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players.In this paper, we propose a generic and principled approach to modeling the interaction between the ball and the players while also imposing appropriate physical constraints on the ball's trajectory.We show that our approach, formulated in terms of a Mixed Integer Program, is more robust and more accurate than several state-of-the-art approaches on real-life volleyball, basketball, and soccer sequences.

show abstract

“…Leo et al [8] refer to a video-based soccer analysis of players and objects. In their approach, the ball and players are tracked by six video cameras.…”

Section: Related Workmentioning

confidence: 99%

Herakles

Michelsen

Brand

Cordes

et al. 2015

Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

View full text Add to dashboard Cite

Tactical decisions profoundly characterize team sports like soccer or basketball. Analyses of matches and training sessions (e.g., mileage or pass completion rate of a player) become more and more important for those tactical decisions. Most of the analyses are video-based, resulting in high operating expenses. Additionally, a highly specialized system with a huge amount of system resources like processors and memory is needed. Typically, analysts present the results of the video data analysis in time-outs (e.g., in the half-time break of a soccer match). However, coaches often desire to view statistics in real-time during the match.In this paper, we demonstrate Herakles, a system for live sport analysis using streaming sensor data and a Peer-toPeer network of conventional and low-cost private machines. Since sensor data is typically of high volume and velocity, Herakles uses OdysseusP2P, a distributed data stream management system, for processing these streams in real-time.Since the results of the data stream processing are intended for coaches, the front-end of Herakles is an application for mobile devices like smartphones or tablets. With Herakles, a coach is able to view individual sport statistics during the game at the sideline to make immediate tactical decisions.

show abstract

Real-time multiview analysis of soccer matches for understanding interactions between ball and players

Cited by 22 publications

References 22 publications

Classification of Passes in Football Matches Using Spatiotemporal Data

Classification of Passes in Football Matches Using Spatiotemporal Data

What Players do with the Ball: A Physically Constrained Interaction Modeling

Herakles

Contact Info

Product

Resources

About