Robust dominant color region detection and color-based applications for sports video

This paper proposes a novel automatic dominant color region detection algorithm that is robust to temporal variations in the dominant color due to field, weather, and lighting conditions throughout a sports video. The algorithm automatically learns the dominant color statistics of the field independent of the sports type, and updates color statistics throughout a sporting event by using two color spaces, a control space and a primary space. The robustness of the algorithm results from adaptation of the statistics of the dominant color in the primary space with drift protection using the control space, and fusion of the information from two spaces. We also propose novel and generic color-based algorithms for referee, player-of-interest, and play-break event detection in sports video. The efficiency of the proposed algorithms is demonstrated over a dataset of various sports video, including basketball, football, golf, and soccer video.