Abstract
Match analysis has become an important task in everyday work at professional soccer clubs in order to improve team performance. Video analysts regularly spend up to several days analyzing and summarizing matches based on tracked and annotated match data. Although there already exists extensive capabilities to track the movement of players and the ball from multimedia data sources such as video recordings, there is no capability to sufficiently detect dynamic and complex events within these data. As a consequence, analysts have to rely on manually created annotations, which are very time-consuming and expensive to create. We propose a novel method for the semi-automatic definition and detection of events based entirely on movement data of players and the ball. Incorporating Allen’s interval algebra into a visual analytics system, we enable analysts to visually define as well as search for complex, hierarchical events. We demonstrate the usefulness of our approach by quantitatively comparing our automatically detected events with manually annotated events from a professional data provider as well as several expert interviews. The results of our evaluation show that the required annotation time for complete matches by using our system can be reduced to a few seconds while achieving a similar level of performance.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
International Football Association Board: Laws of the game (2018/2019). http://theifab.com/document/laws-of-the-game. Accessed 02 Aug 2018
Chen, M., Zhang, C., Chen, S.C.: Semantic event extraction using neural network ensembles, pp. 575–580. IEEE, September 2007. https://doi.org/10.1109/ICSC.2007.75
de Sousa Júnior, S.F., de Albuquerque Araújo, A., Menotti, D.: An overview of automatic event detection in soccer matches, pp. 31–38. IEEE, January 2011. https://doi.org/10.1109/WACV.2011.5711480
Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Trans. Image Process. 12(7), 796–807 (2003)
Gudmundsson, J., Wolle, T.: Towards automated football analysis: algorithms and data structures. In: Proceedings of the 10th Australasian Conference on Mathematics and Computers in Sport. Citeseer (2010)
Jensen, J.C.C.: Event detection in soccer using spatio-temporal data. Ph.D. thesis, Aarhus Universitet, Datalogisk Institut (2015)
Kempe, S.: Häufige Muster in zeitbezogenen Daten. Ph.D. thesis, Otto-von-Guericke University Magdeburg, Germany (2008). http://edoc.bibliothek.uni-halle.de/receive/HALCoRe_document_00005803
Kolekar, M.H., Palaniappan, K., Sengupta, S., Seetharaman, G.: Semantic concept mining based on hierarchical event detection for soccer video indexing. J. Multimed. 4(5), 298–312 (2009). https://doi.org/10.4304/jmm.4.5.298-312
Stein, M., et al.: Bring it to the pitch: combining video and movement data to enhance team sport analysis. IEEE Trans. Vis. Comput. Graph. 24(1), 13–22 (2018)
Wang, T., Li, J., Diao, Q., Hu, W., Zhang, Y., Dulong, C.: Semantic event detection using conditional random fields, p. 109. IEEE (2006). https://doi.org/10.1109/CVPRW.2006.190
Tavassolipour, M., Karimian, M., Kasaei, S.: Event detection and summarization in soccer videos using Bayesian network and copula. IEEE Trans. Circ. Syst. Video Technol. 24(2), 291–304 (2014)
Tovinkere, V., Qian, R.: Detecting semantic events in soccer games: towards a complete solution, pp. 833–836. IEEE (2001). https://doi.org/10.1109/ICME.2001.1237851
Visvalingam, M., Whyatt, J.D.: Line generalisation by repeated elimination of points. Cartogr. J. 30(1), 46–51 (1993)
Wickramaratna, K., Chen, M., Chen, S.-C., Shyu, M.-L.: Neural network based framework for goal event detection in soccer videos, pp. 21–28. IEEE (2005). https://doi.org/10.1109/ISM.2005.83
Tong, X.-F., Lu, H.-Q., Liu, Q.-S.: A three-layer event detection framework and its application in soccer video, pp. 1551–1554. IEEE (2004). https://doi.org/10.1109/ICME.2004.1394543
Yu, X., Xu, C., Leong, H.W., Tian, Q., Tang, Q., Wan, K.W.: Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video, p. 10 (2003)
Zheng, M., Kudenko, D.: Automated event recognition for football commentary generation. Int. J. Gaming Comput.-Mediat. Simul. 2(4), 67–84 (2010). https://doi.org/10.4018/jgcms.2010100105
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Stein, M., Seebacher, D., Karge, T., Polk, T., Grossniklaus, M., Keim, D.A. (2019). From Movement to Events: Improving Soccer Match Annotations. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_11
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DOI: https://doi.org/10.1007/978-3-030-05710-7_11
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