A two-fold transformation model for human action recognition using decisive pose

Abstract Human action recognition in videos is a tough task due to the complex background, geometrical transformation and an enormous volume of data. Hence, to address these issues, an effective algorithm is developed, which can identify human action in videos using a single decisive pose. To achieve the task, a decisive pose is extracted using optical flow, and further, feature extraction is done via a two-fold transformation of wavelet. The two-fold transformation is done via Gabor Wavelet Transform (GWT) and Ridgelet Transform (RT). The GWT produces a feature vector by calculating first-order statistics values of different scale and orientations of an input pose, which have robustness against translation, scaling and rotation. The orientation-dependent shape characteristics of human action are computed using RT. The fusion of these features gives a robust unified algorithm. The effectiveness of the algorithm is measured on four publicly datasets i.e. KTH, Weizmann, Ballet Movement, and UT Interaction and accuracy reported on these datasets are 96.66%, 96%, 92.75% and 100%, respectively. The comparison of accuracies with similar state-of-the-arts shows superior performance.