Classification of sports types from tracklets

Publikation: Konferencebidrag uden forlag/tidsskriftKonferenceabstrakt til konferenceForskningpeer review

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Resumé

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 %.
OriginalsprogEngelsk
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
http://www.kdd.org/kdd2014/

Workshop

WorkshopKDD Workshop on Large-scale Sports Analytics
Nummer20
LandUSA
ByNew York
Periode24/08/201427/08/2014
Internetadresse

Fingerprint

Sports
Discriminant analysis
Kalman filters
Trajectories
Experiments

Citer dette

Gade, R., & Moeslund, T. B. (2014). Classification of sports types from tracklets. Abstract fra KDD Workshop on Large-scale Sports Analytics, New York, USA.
Gade, Rikke ; Moeslund, Thomas B. / Classification of sports types from tracklets. Abstract fra KDD Workshop on Large-scale Sports Analytics, New York, USA.4 s.
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Gade, R & Moeslund, TB 2014, 'Classification of sports types from tracklets', KDD Workshop on Large-scale Sports Analytics, New York, USA, 24/08/2014 - 27/08/2014.

Classification of sports types from tracklets. / Gade, Rikke; Moeslund, Thomas B.

2014. Abstract fra KDD Workshop on Large-scale Sports Analytics, New York, USA.

Publikation: Konferencebidrag uden forlag/tidsskriftKonferenceabstrakt til konferenceForskningpeer review

TY - ABST

T1 - Classification of sports types from tracklets

AU - Gade, Rikke

AU - Moeslund, Thomas B.

PY - 2014/8

Y1 - 2014/8

N2 - 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 %.

AB - 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 %.

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Gade R, Moeslund TB. Classification of sports types from tracklets. 2014. Abstract fra KDD Workshop on Large-scale Sports Analytics, New York, USA.