Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyMoraisGoldFerrRoch:2012:AuTrIn
TitleAutomatic tracking of indoor soccer players using videos from multiple cameras
FormatDVD, On-line.
Access Date2021, Jan. 28
Number of Files1
Size1599 KiB
Context area
Author1 Morais, Erikson Freitas de
2 Goldenstein, Siome
3 Ferreira, Anselmo
4 Rocha, Anderson
Affiliation1 IC-UNICAMP
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:29 :: administrator -> :: 2012
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsTracking, Soccer, Particle Filter, Data Fusion.
AbstractIndoor soccer has been of tactical and scientific interest, with applications dedicated to analyze tactical and physiological factors and also physical training. In both cases, the analysis is based on player tracking, done with human supervision. This paper presents an automatic tracking method which shows the trajectories of indoor soccer players during the game and saving skilled labor during the process. For this, we use a predictive filter to model the motion and the observation of multiple stationary cameras, strategically positioned around the court. We associate a particle filter to a robust probabilistic observation model with the measurement in court coordinates. The observation model proposed is based on data fusion across multiple camera coordinates and projected onto the court plane, creating a multimodal and bidirectional probability function, which represents the potential localization of players in the court plane. The probability function uses an appearance model to observe player's location, distinguishing very close players and yielding good weights in the observation model. The experimental results show tracking errors below 70 centimeters in most cases and indicate the potential of the method to help sports teams.
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