Instant Action Recognition

In this paper, we present an efficient system for action recognition from very short sequences. For action recognition typically appearance and/or motion information of an action is analyzed using a large number of frames. This is a limitation if very fast actions (e.g., in sport analysis) have to be analyzed. To overcome this limitation, we propose a method that uses a single-frame representation for actions based on appearance and motion information. In particular, we estimate Histograms of Oriented Gradients (HOGs) for the current frame as well as for the corresponding dense flow field. The thus obtained descriptors are efficiently represented by the coefficients of a Non-negative Matrix Factorization (NMF). Actions are classified using an one-vs-all Support Vector Machine. Since the flow can be estimated from two frames, in the evaluation stage only two consecutive frames are required for the action analysis. Both, the optical flow as well as the HOGs, can be computed very efficiently. In the experiments, we compare the proposed approach to state-of-the-art methods and show that it yields competitive results. In addition, we demonstrate action recognition for real-world beach-volleyball sequences.