Real-time action recognition based on cumulative Motion shapes
Although several methods for action recognition have been proposed in the literature, many of them have limitations in terms of applicability in real-life situations. Despite satisfactory accuracy rates achieved by a number of methods, an effective action recognition system requires workability in real time. However, this feature usually comes along with certain loss in accuracy. In this paper, we present a real-time action recognition method that achieves state-of-the-art accuracy. By accumulating shape information over a sliding window on the video frames, the method extracts and processes silhouettes with little computational effort. Simple descriptors are computed over the shapes and applied on a fast configuration of classifiers. Experiments are conducted on three public data sets and the results demonstrate the effectiveness of the method in terms of accuracy and speed.