Human Action Recognition Using Hybrid Centroid Canonical Correlation Analysis

Abstract

Human action recognition is a hot research topic in image analysis and computer vision. In this paper, we propose Hybrid Centroid Canonical Correlation Analysis (HCCCA) and multi-set HCCCA for multimodal information analysis and fusion. Furthermore, we present a novel human action recognition framework by using multi-set HCCCA to fuse multimodal features, which include the hierarchal pyramid Depth Motion Map (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 Action 3D 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.