Integrated approach for human action recognition using edge spatial distribution, direction pixel and R-transform

In this article, a simple yet proficient approach for the recognition of human action and Activity is presented. This method is based on the integration of translation and rotation of the human body. The proposed framework undergoes three major steps: (i) the shape of the human action/activity is represented through the computation of average energy images using edge spatial distribution of gradients along with the directional variation of the pixel values, (ii) the orientation-based rotational information of the human action is computed through R-transform and (iii) a descriptor is developed by the fusion of translational features with rotational features. The fusion of features possesses the advantages exhibited by both local and global features of the silhouette and thus provides the discriminating feature representation for human activity recognition. The performance of descriptor is evaluated through a hybrid approach of support vector machine and the nearest neighbour classifiers on standard data set. The proposed method has shown superior results in terms of recognition accuracy in comparison with other state-of-the-art methods.