Human action recognition using temporal hierarchical pyramid of depth motion map and KECA

Human action recognition is one of the challenging research problems in computer vision. In this paper, we propose a novel approach for human action recognition. The proposed approach employs a temporal hierarchical pyramid of depth motion map to capture the temporal variations over the time. In addition, Kernel Entropy Component Analysis (KECA) is used to reduce the dimension and to enhance the discriminatory power for action recognition. The proposed method was evaluated using two datasets, MSR-Action 3D dataset and MSR-Gesture 3D dataset. The experimental results demonstrated that the proposed method can achieve a higher average accuracy compared to several existing methods.