Real-time tracking of a tennis ball by combining 3D data and domain knowledge

Computer vision is steadily gaining importance in many research fields, as its applications expand from traditional fields situation analysis and scene understanding in video surveillance to other scenarios. The sportive context can represent a perfect test-bed for many machine vision algorithms because of the large availability of visual data brought by wide spread cameras on a relatively high number of courts. In this paper we introduce a tennis ball detection and tracking method that exploits domain knowledge to effectively recognize ball positions and trajectories. A peculiarity of this approach is that it starts from a sparse but cluttered point cloud that evolves over time, basically working on 3D samples only. Experiments on real data demonstrate the effectiveness of the algorithm in terms of tracking accuracy and path following capability.