Paper Info
Title
Enhanced Spatial and Extended Temporal Graph Convolutional Network for Skeleton-Based Action Recognition.
Abstract
In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the temporal cues comprehensively. Moreover, simply connecting the spatial graph convolution layer (GCL) and the temporal GCL in series is not the optimal solution. To this end, we propose a novel enhanced spatial and extended temporal graph convolutional network (EE-GCN) in this paper. Three convolution kernels with different sizes are chosen to extract the discriminative temporal features from shorter to longer terms. The corresponding GCLs are then concatenated by a powerful yet efficient one-shot aggregation (OSA) + effective squeeze-excitation (eSE) structure. The OSA module aggregates the features from each layer once to the output, and the eSE module explores the interdependency between the channels of the output. Besides, we propose a new connection paradigm to enhance the spatial features, which expand the serial connection to a combination of serial and parallel connections by adding a spatial GCL in parallel with the temporal GCLs. The proposed method is evaluated on three large scale datasets, and the experimental results show that the performance of our method exceeds previous state-of-the-art methods.
Year
DOI
Venue
2020
10.3390/s20185260
SENSORS
Keywords
DocType
Volume
skeleton-based action recognition,graph convolution network,enhanced spatial,extended temporal
Journal
20
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Fanjia Li100.34
Juanjuan Li27114.17
Aichun Zhu300.68
Yonggang Xu431.80
Hongsheng Yin5144.75
Gang Hua600.68