View-Independent Discriminant Analysis with Gradient Self-Similarities for Action Recognition under Multiple Views

Abstract

In Multi-View Human Action Recognition, the actions are not of single view and hence to achieve an effective recognition performance under multi-view actions, there is a need of multi-view subclass discrimination analysis. Based on this inspiration, this paper proposed a novel action recognition framework based on the Subclass Discriminant Analysis (SDA), an extended version of Linear Discriminant Analysis (LDA). Further, a new key frames selection method is proposed based on Self-Similarity Matrix (SSM), called as Gradient SSM (GSSM). Once the key frames are selected, a composite feature set is extracted through three different set filters such as Gaussian Filter, Gabor filter and Wavelet Filter. Next, the SDA obtain an optimal feature subspace for every action under multiple Views. Finally the SVM algorithm classifies the action. The proposed framework is systematically evaluated on IXMAS dataset and NIXMAS dataset. Experimental results enumerate that our method outperforms the conventional techniques in terms of recognition accuracy.