Global Spatio-Temporal Attention for Action Recognition Based on 3D Human Skeleton Data

Abstract

The human skeleton joints captured by RGB-D camera are widely used in action recognition for its robust and comprehensive 3D information. Presently, most action recognition methods based on skeleton joints treat all skeletal joints with the same importance spatially and temporally. However, the contributions of skeletal joints vary significantly. Hence, a GL-LSTM+Diff model is proposed to improve the recognition of human actions. A global spatial attention (GSA) model is proposed to express the different weights for different skeletal joints to provide precise spatial information for human action recognition. The accumulative learning curve (ALC) model is introduced to highlight which frames contribute most to the final decision making by giving varying temporal weights to each intermediate accumulated learning results. By integrating the proposed GSA (for spatial information) and ALC (for temporal processing) models into the LSTM framework and taking the human skeletal joints as inputs, a global spatio-temporal action recognition framework (GL-LSTM) is constructed to recognize human actions. Diff is introduced as the preprocessing method to enhance the dynamic of the features, thus to get distinguishable features in deep learning. Rigorous experiments on the largest dataset NTU RGB+D and the common small dataset SBU show that the algorithm proposed in this paper outperforms other state-of-the-art methods.