Baseball Playfield Segmentation Using Adaptive Gaussian Mixture Models

Playfield is one of main parts appearing in typical scenes of sports video. Generally, the playfield always has very distinctive attributes. For baseball, the playfield is composed of grass and soil. The colors of grass and soil are selected as a feature to segment the playfield in our work. However, playfield colors demonstrate significant variations, which may cause a large amount of segmentation errors for color-based segmentation. In this paper, we present a new method of grass-soil playfield segmentation for baseball videos based on an adaptive Gaussian mixture model. To improve segmentation accuracy, a particular GMM model is obtained by automatic training directly from sample data for each baseball game. The simulation results indicate that it can achieve very low error rates.