Robust Playfield Segmentation using MAP Adaptation

A vital task in sports video annotation is to detect and segment areas of the playfield. This is an important first step in player or ball tracking and detecting the location of the play on the playfield. In this paper we present a technique using statistical models, Gaussian mixture models (GMMs) and Maximum a Posteriori (MAP) adaptation. This involves first creating a generic model of the playfield colour and then using unsupervised MAP adaptation to adapt this model to the colour of the playfield in each game. This technique provides a robust and accurate segmentation of the playfield. To demonstrate the robustness of the method we tested it on a number of different sports that have grass playfields, rugby, soccer and field hockey.


Published in:
Proc. 17th International Conference on Pattern Recognition (ICPR 2004)
Presented at:
Proc. 17th International Conference on Pattern Recognition (ICPR 2004)
Year:
2004
Publisher:
Cambridge, United Kingdom
Keywords:
Note:
IDIAP-RR 03-77
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

n/a:
Download fulltextPDF
External link:
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)