Temporal Feature Weighting for Prototype-Based Action Recognition

In action recognition recently prototype-based classification methods became popular. However, such methods, even showing competitive classification results, are often limited due to too simple and thus insufficient representations and require a long-term analysis. To compensate these problems we propose to use more sophisticated features and an efficient prototype-based representation allowing for a single-frame evaluation. In particular, we apply four feature cues in parallel (two for appearance and two for motion) and apply a hierarchical k-means tree, where the obtained leaf nodes represent the prototypes. In addition, to increase the classification power, we introduce a temporal weighting scheme for the different information cues. Thus, in contrast to existing methods, which typically use global weighting strategies (i.e., the same weights are applied for all data) the weights are estimated separately for a specific point in time. We demonstrate our approach on standard benchmark datasets showing excellent classification results. In particular, we give a detailed study on the applied features, the hierarchical tree representation, and the influence of temporal weighting as well as a competitive comparison to existing state-of-the-art methods.