Classification of sports types from tracklets

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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 towards 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 classification 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 %.
Publikationsdatoaug. 2014
Antal sider4
StatusUdgivet - aug. 2014
BegivenhedKDD Workshop on Large-scale Sports Analytics - New York, USA
Varighed: 24 aug. 201427 aug. 2014
Konferencens nummer: 20


WorkshopKDD Workshop on Large-scale Sports Analytics
ByNew York

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