Spatio-Temporal learning of basketball offensive strategies

Video-based group behavior analysis is drawing attention to its rich applications in sports, military, surveillance and biological observations. The recent advances in tracking techniques, based on either computer vision methodology or hardware sensors, further provide the opportunity of better solving this challenging task. Focusing specifically on the analysis of basketball offensive strategies, we introduce a systematic approach to establishing unsupervised modeling of group behaviors. In view that a possible group behavior (offensive strategy) could be of different duration and represented by dynamic player trajectories, the crux of our method is to automatically divide training data into meaningful clusters and learn their respective spatio-Temporal model, which is established upon Gaussian mixture regression to account for intra-class spatio-Temporal variations. The resulting strategy representation turns out to be exible that can be used to not only establish the discriminant functions but also improve learning the models. We demonstrate the usefulness of our approach by exploring its effectiveness in analyzing a set of given basketball video clips.