作者：张亮[等]著
页数：172页
出版社：西安电子科技大学出版社
出版日期：2022
ISBN：9787560665399
电子书格式：pdf/epub/txt
内容简介
本书重点从智能无人设备对人的自然行为，如常用手势、动作等，以及对场景中物体、物体间关系建模方法角度出发，讲解最新的基于深度学习的网络构建研究成果，为相关领域科研工作者提供参考。
目录
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
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
