Learning discriminative representation for skeletal action recognition using LSTM networks

Abstract

Human action recognition based on 3D skeleton data is a rapidly growing research area in computer vision due to their robustness to variations of viewpoint, human body scale and motion speed. Recent studies suggest that recurrent neural networks (RNNs) or convolutional neural networks (CNNs) are very effective to learn discriminative features of temporal sequences for classification. However, in prior models, the RNN-based method has a complicated multi-layer hierarchical architecture, and the CNN-based methods learn the contextual feature on fixed temporal scales. In this paper, we propose a framework which is simple and able to select temporal scales automatically with a single layer LSTM for skeleton based action recognition. Experimental results on three benchmark datasets show that our approach achieves the state-of-the-art performance compared to recent models.