Spatio-temporal learning of basketball offensive strategies

Chinghang Chen

Abstract:

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 flexible
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.