Abstract
Automatic analysis of video is important in order to process and exploit large amounts of data, e.g. for sports analysis. Classification of sports types is one of the first steps to- wards a fully automatic analysis of the activities performed at sports arenas. In this work we test the idea that sports types can be classified from features extracted from short trajectories of the players. From tracklets created by a Kalman filter tracker we extract four robust features; Total distance, lifespan, distance span and mean speed. For clas- sification we use a quadratic discriminant analysis. In our experiments we use 30 2-minutes thermal video sequences from each of five different sports types. By applying a 10- fold cross validation we obtain a correct classification rate of 94.5 %.
Original language | English |
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Publication date | Aug 2014 |
Number of pages | 4 |
Publication status | Published - Aug 2014 |
Event | KDD Workshop on Large-scale Sports Analytics - New York, United States Duration: 24 Aug 2014 → 27 Aug 2014 Conference number: 20 http://www.kdd.org/kdd2014/ |
Workshop
Workshop | KDD Workshop on Large-scale Sports Analytics |
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Number | 20 |
Country/Territory | United States |
City | New York |
Period | 24/08/2014 → 27/08/2014 |
Internet address |