Action recognition using spatio-temporal regularity based features

In this paper, a novel feature for capturing information in a spatio-temporal volume based on regularity flow is presented for action recognition. The regularity flow describes the direction of least intensity change within a spatio-temporal volume. Our feature consists of weighted histograms of the computed regularity flow around selected interest points. We then apply this new feature to recognizing actions with experiments on known benchmark dataset. A more discriminating representation of spatio-temporal volume is obtained by using the feature descriptors with the bag of words model. Action recognition is performed by using this new representation with a trained support vector machine. We show that by utilizing regularity flow based features, recognition can be performed with better performance than the best known features. Additionally, results suggest that our descriptor captures information otherwise not harnessed by existing methods.