Human action recognition via multiview discriminative analysis of canonical correlations

This paper proposes a novel Multiview Discriminative Analysis of Canonical Correlations (MDACC) for multiview learning. The proposed MDACC can capture discriminative features. Furthermore, we present a human action recognition framework by using MDACC to fuse multimodal features, which include the hierarchical Pyramid of Depth Motion Map (HP-DMM) for the depth images, the Histogram of Oriented Displacement (HOD) for the skeleton, and the statistical measurements for the accelerometer. The proposed framework was evaluated using two datasets MSR-Action3D dataset and UTD multimodal human action dataset. The experimental results demonstrated that the proposed framework can achieve a higher average accuracy compared to several existing methods.