Classification of sports types from tracklets
Rikke Gade, Thomas Baltzer Moeslund · VBN Forskningsportal (Aalborg Universitet) · 2014
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 %.