TY - JOUR

T1 - Recognition of repetitive movement patterns—the case of football analysis

AU - Feuerhake, Udo

N1 - Funding information: Acknowledgments: The funding of the “q-trajectories” project by DFG as the origin of this research is gratefully Aacckknnoowwlleeddggemd.eTnhtse: pTuhbelicfautinodni nogf tohfis tahretic“lqe-wtraajse cfutonrdieesd”bpyrothjeecOt pbeynDAFcGcesassFtuhnedoorfi gthine Lofeibthniisz rUenseivaercrhsitäist gratefully acknowledged. The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover. Conflicts of Interest: The author declares no conflict of interest. Conflicts of Interest: The author declares no conflict of interest.

PY - 2016/11/9

Y1 - 2016/11/9

N2 - Analyzing sports like football is interesting not only for the sports team itself, but also for the public and the media. Both have recognized that using more detailed analyses of the teams’ behavior increases their attractiveness and also their performance. For this reason, the games and the individual players are recorded using specially developed tracking systems. The tracking solution usually comes with elementary analysis software allowing for basic statistical information extraction. Going beyond these simple statistics is a challenging task. However, it is worthwhile when it provides a better view into the tactics of team or the typical movements of an individual player. In this paper an approach for the recognition of movement patterns as an advanced analysis method is presented, which uses the players’ trajectories as input data. Besides individual movement patterns it is also able to detect patterns in relation to group movements. A detailed description is followed by a discussion of the approach, where different experiments on real trajectory datasets, even from other contexts than football, show the method’s benefits and features.

AB - Analyzing sports like football is interesting not only for the sports team itself, but also for the public and the media. Both have recognized that using more detailed analyses of the teams’ behavior increases their attractiveness and also their performance. For this reason, the games and the individual players are recorded using specially developed tracking systems. The tracking solution usually comes with elementary analysis software allowing for basic statistical information extraction. Going beyond these simple statistics is a challenging task. However, it is worthwhile when it provides a better view into the tactics of team or the typical movements of an individual player. In this paper an approach for the recognition of movement patterns as an advanced analysis method is presented, which uses the players’ trajectories as input data. Besides individual movement patterns it is also able to detect patterns in relation to group movements. A detailed description is followed by a discussion of the approach, where different experiments on real trajectory datasets, even from other contexts than football, show the method’s benefits and features.

KW - Football analysis

KW - Pattern recognition

KW - Spatio-temporal analysis

KW - Trajectory

UR - http://www.scopus.com/inward/record.url?scp=85032364825&partnerID=8YFLogxK

U2 - https://doi.org/10.3390/ijgi5110208

DO - https://doi.org/10.3390/ijgi5110208

M3 - Article

VL - 5

JO - ISPRS International Journal of Geo-Information

JF - ISPRS International Journal of Geo-Information

SN - 2220-9964

IS - 11

M1 - 208

ER -