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Chapter 1 Human Action Recognition Using MultMayer Codebooks of Key Poses and Atomic Motions 1.1 Introduction 1.2 Related Work 1.2.1 Feature Representation 1.2.2 Classification Model 1.3 Construction of Multi-layer Codebook 1.3.1 Feature Representation 1.3.2 Feature Sequence Segmentation 1,3.3 Pose-layer Codebook 1.3.4 Motion-layer Codebook 1.3.5 Multi-layer Codebook Construction 1.4 Classification Methods 1.4.1 Naive Bayes Nearest Nei or 1.4.2 Support Vector Machine 1.4.3 Random Forest 1.5 Experimental Results 1.5.1 Experiments on the CAD-60 dataset 1.5.2 Experiments on the MSRC-12 dataset 1.5.3 Discussion 1.6 Conclusion and Future Work Acknowledgements References Chapter 2 Topology-learnable Graph Convolution for Skeleton-based Action Recognition 2.1 Introduction 2.2 Related Work 2.2.1 Graph Convolutional Network for Action Recognition 2.2.2 Adaptive Graph Convolution 2.3 Topology-learnable Graph Convolution 2.3.1 Graph Convolution 2.3.2 Graph Topology Analysis 2.3.3 Topology-learnable Graph Convolution 2.3.4 Topology-learnable GCNs 2.4 Experiments 2.4.1 Datasets 2.4.2 Ablation Study 2.4.3 Comparison with the State-of-the-art Methods 2.4.4 Discussion 2.5 Conclusion Acknowledgements References Chapter 3 Recurrent Graph Convolutional Networks for Skeleton-based Action Recognition 3.1 Introduction 3.2 Related Work 3.2.1 Graph Convolution for Action Recognition 3.2.2 LSTM on Graphs 3.3 Recurrent Graph Convolutional Network 3.3.1 Graph Convolution 3.3.2 Adaptive Graph Convolution 3.3.3 Recurrent Graph Convolution 3.3.4 Recurrent Graph Convolutional Network