Generalized playfield segmentation of sport videos using color features

This paper proposes a generalized method for playfield segmentation of various sport videos. It first estimates the probability density function (pdf) of color components of an image frame. Two hill-climbing schemes, which employ two-dimensional pdf and one-dimensional pdf, respectively, are proposed for clustering. Next, a novel algorithm that utilizes the domain knowledge of sport playfields is developed to merge those clusters into four color classes: red, green, blue, and gray. Finally, a simple scheme fuses small regions into their adjacent large regions to obtain the segmentation result. The experimental results indicate that the proposed method effectively segments the playfield regions of various sport videos.

Research highlights
► We propose a method to segment playfields for various sport videos.
► Two schemes which employ pdf of color features are first proposed for clustering.
► A novel scheme is then developed to merge those clusters into four color classes.
► The segmentation based on the four classes effectively segments the playfield.