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.