Abstract
Human action recognition (HAR) is an active and crucial field of computer vision due to its various applications, such as smart surveillance and human-computer interaction. Recently, the human skeleton, which is compact and intuitive for representing actions and body movements, has been widely used in numerous HAR frameworks. Despite the great success of the skeleton-based HAR, several challenges remain, such as intra-class variability and inter-class similarity. In this paper, we address this task by first proposing a discriminative representation of the action-snippet (i.e., the very short sequence) that captures meaningful characteristics of human pose and body transition. We then employ adequate deep sequential neural networks (DSNNs) to thoroughly learn the temporal relation of action-snippets in a whole sequence. In experiments, the results show that the proposed approach achieves high recognition rates on benchmark datasets while maintaining good computational efficiency (i.e., lightweight networks and high recognition speed).