Deep learning for skeleton-based action recognition

Abstract

In order to further improve the recognition rate of skeleton action recognition, and to break the limitation that most of the previous deep learning-based methods have the input content of human joint coordinates, we propose a human skeleton action recognition algorithm combining geometric features and LSTM networks. In this paper, we propose a human skeleton action recognition algorithm that combines geometric features with LSTM network. The algorithm selects geometric features based on the distances between joints and selected lines as the input of the network, and introduces a time-selective LSTM network for training. The ability to select the most recognizable temporal features using the time-selective LSTM network is demonstrated in the SBU Interaction dataset and UT Kinect dataset, achieving 99.46% and 99.30% recognition rates, respectively. The experimental results demonstrate the effectiveness of the method for human skeleton-based action recognition.