Distributed Crowd-based Annotation of Soccer Games using Mobile Devices

Soccer is one of the most loved sports in the world. Millions of people either follow the sport or are actually involved in its practice. Soccer also moves huge financial amounts every year and therefore teams always thrive to be better than the competition. New technologies have become a common place both in the preparation of the games and on the analysis of the games after they are concluded. In this paper, the authors will present a developed system, based on the usage of distributed mobile devices, that will enable the annotation of soccer matches, either in real time or after the matched is concluded (through the observation of other media). The capture of relevant events in the game can be used to better analyse the game and the performance of individual players fostering improvements and better decisions in the future. The application is implemented in the Android platform so that it can be easily installed by typical soccer fans empowering them as match annotators. This crowd of annotators, although not experts, can collectively provide a robust and rich annotation for soccer matches.

[1]  Cathal Gurrin,et al.  Muithu: A Touch-Based Annotation Interface for Activity Logging in the Norwegian Premier League , 2014, MMM.

[2]  Carsten Griwodz,et al.  Soccer video and player position dataset , 2014, MMSys '14.

[3]  Vikas Sindhwani,et al.  Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria , 2009, HLT-NAACL 2009.

[4]  Charles Perin,et al.  Real-Time Crowdsourcing of Detailed Soccer Data , 2013 .

[5]  Krisztina Mártha,et al.  Inter-Operator Reliability of Dental Morphometric Measurements , 2018, Journal of Interdisciplinary Medicine.

[6]  Alberto Del Bimbo,et al.  Semantic annotation of soccer videos by visual instance clustering and spatial/temporal reasoning in ontologies , 2010, Multimedia Tools and Applications.

[7]  Carsten Griwodz,et al.  Bagadus: an integrated system for arena sports analytics: a soccer case study , 2013, MMSys.

[8]  Stefanie Nowak,et al.  How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation , 2010, MIR '10.

[9]  Shu-Yuan Chen,et al.  Automatic Broadcast Soccer Video Analysis, Player Detection, and Tracking Based on Color Histogram , 2013 .

[10]  Juan Carlos Pastor-Vicedo,et al.  Performance indicators as a resource for the selection of talented football players , 2017 .

[11]  Alberto Del Bimbo,et al.  Semantic annotation of soccer videos: automatic highlights identification , 2003, Comput. Vis. Image Underst..

[12]  J. Fernández-Río,et al.  Talent detection and development in soccer: a review. , 2014 .

[13]  Will G. Hopkins,et al.  Inter-operator reliability of live football match statistics from OPTA Sportsdata , 2013 .