Recognizing offensive strategies from football videos

We address the problem of recognizing offensive play strategies from American football play videos. Specifically, we propose a probabilistic model which describes the generative process of an observed football play and takes into account practical issues in real football videos, such as difficulty in identifying offensive players, view changes, and tracking errors. In particular, we exploit the geometric properties of nonlinear spaces of involved variables and design statistical models on these manifolds. Then recognition is performed via 'analysis-by-synthesis' technique. Experiments on a newly established dataset of American football videos demonstrate the effectiveness of the approach.