Two-stream temporal enhanced Fisher vector encoding for skeleton-based action recognition

Abstract

The key to skeleton-based action recognition is how to extract discriminative features from skeleton data. Recently, graph convolutional networks (GCNs) are proven to be highly successful for skeleton-based action recognition. However, existing GCN-based methods focus on extracting robust features while neglecting the information of feature distributions. In this work, we aim to introduce Fisher vector (FV) encoding into GCN to effectively utilize the information of feature distributions. However, since the Gaussian Mixture Model (GMM) is employed to fit the global distribution of features, Fisher vector encoding inevitably leads to losing temporal information of actions, which is demonstrated by our analysis. To tackle this problem, we propose a temporal enhanced Fisher vector encoding algorithm (TEFV) to provide more discriminative visual representation. Compared with FV, our TEFV model can not only preserve the temporal information of the entire action but also capture fine-grained spatial configurations and temporal dynamics. Moreover, we propose a two-stream framework (2sTEFV-GCN) by combining the TEFV model with the GCN model to further improve the performance. On two large-scale datasets for skeleton-based action recognition, NTU-RGB+D 60 and NTU-RGB+D 120, our model achieves state-of-the-art performance.