Detector-less ball localization using context and motion flow analysis

Abstract

We present a technique for estimating the location of the ball during a basketball game without using a detector. The technique is based on the analysis of the dynamics in the scene and allows us to overcome the challenges due to frequent occlusions of the ball and its similarity in appearance with the background. Based on the assumption that the ball is the point of focus of the game and that the motion flow of the players is dependent on its position during attack actions, the most probable candidates for the ball location are extracted from each frame. These candidates are then validated over time using a Kalman filter. Experimental results on a real basketball dataset show that the location of the ball can be estimated with an average accuracy of 82%.