Skeleton-based Action Recognition Based on Deep Learning and Grassmannian Pyramids

The accuracy of modern depth sensors, the robustness of skeletal data to illumination variations and the superb performance of deep learning techniques on several classification tasks have sparkled a renewed interest towards skeleton based action recognition. In this paper, we propose a four stream deep neural network based on two types of spatial skeletal features and their corresponding temporal representations extracted by the novel Grassmannian Pyramid Descriptor (GPD). The performance of the proposed action recognition methodology is further enhanced by the use of a meta-learner that takes advantage of the meta knowledge extracted from the processing of the different features. Experiments on several well-known action recognition datasets reveal that our proposed methodology outperforms a number of state-of-the-art skeleton-based action recognition methods.