基于多模态数据的行为和手势识别
Chapter 1 Human Action Recognition Using Multi-layer 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
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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
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张亮，男，汉族，1981年5月生，西安电子科技大学教授，博士生导师，本硕博毕业于浙江大学，现任西安电子科技大学计算机科学与技术学院“嵌入式技术与视觉处理中心”主任，全国计算机学会嵌入式专委会委员，IEEE会员，ACM会员。主要研究方向为深度学习、手势手语识别、场景语义理解、嵌入式多核系统等，作为负责人先后承担国家重点研发计划、国家自然科学基金及企业合作项目多项。在IEEE TNNLS、IEEE TMI、IEEE TIP等本领域重要期刊和NeurIPS、CVPR、ICCV等国际会议上发表论文80余篇，授权专利20余项，获陕西省科技进步二等奖1项，陕西省高等学校科学技术奖一等奖1项。
李宁，男，满族，1990年8月生，中国船舶工业综合技术经济研究院工程师，本硕毕业于北京林业大学，国防科技大学博士生，现任中国船舶工业综合技术经济研究院人因工程中心主任助理、系统研发研究室主任，国家重点渠道人机工程标准化委员秘书，全国人类工效学会会…
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李宁，男，满族，1990年8月生，中国船舶工业综合技术经济研究院工程师，本硕毕业于北京林业大学，国防科技大学博士生，现任中国船舶工业综合技术经济研究院人因工程中心主任助理、系统研发研究室主任，国家重点渠道人机工程标准化委员秘书，全国人类工效学会会…
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This book provides a series of gesture and behavior recognition methods based on multimodal data representation. The data modalities include image data and skeleton data， and the modeling methods include traditional codebook， topological graph， and LSTM architectures. The tasks include single gesture recognition classification， single action recognition classification， continuous gesture classification， complex behavior classif…
查看完整
查看完整
Chapter 1 Human Action Recognition Using Multi-layer 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 Neighbor
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 forAction 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
3．4．1 Datasets
3．4．2 Training Details
3．4．3 Ablation Study
3．4．4 Comparison with the State-of-the-art Methods
3．4．5 Visualization of the Evolved Graph Topologies
3．5 Conclusion
Acknowledgements
References
Chapter 4 Graph-temporal LSTM Networks for Skeleton-based Action Recognition
4．1 Introduction
4．2 Related Work
4．3 GT-LSTM Networks
4．3．1 Pipeline Overview
4．3．2 Topology-learnable ST-GCN
4．3．3 GT-LSTM
4．3．4 GT-LSTM Networks
4．4 Experiments
4．4．1 Datasets
4．4．2 Training Details
4．4．3 Ablation Study
4．4．4 Comparison with the State-of-the-art Methods
4．5 Conclusion
References
Chapter 5 Spatio-temporal Interaction Graph Parsing Networks for Human-object Interaction Recognition
5．1 Introduction
5．2 Related Work
5．3 Overview
5．4 Proposed Approach
5．4．1 Video Feature Extraction
5．4．2 Spatio-temporal Interaction Graph Parsing
5．4．3 Inference
5．4．4 Implementation Details
5．5 Experiments
5．5．1 Dataset
5．5．2 Ablation Study
5．5．3 Comparison with the State-of-the-arts Methods
5．5．4 Visualization of Parsed Graphs
5．6 Conclusion
Acknowledgements
References
Chapter 6 Learning Spatio-temporal Features Using 3DCNN and Convolutional LSTM For Gesture Recognition
6．1 Introduction
6．2 Related Work
6．3 Method
6．3．1 2D Spatio-temporal Feature MapLearning
6．3．2 Classification Based on the 2D Feature Maps
6．3．3 Network Training
6．4 Experiments
6．4．1 Datasets
6．4．2 Implementation
6．4．3 Architecture Analysis
6．4．4 Comparison with the State-of-the-art Methods
6．5 Conclusion
Acknowledgements
References
……
Chapter 7 Multimodal Gesture Recognition Using 3D Convoluhon and Convolutional LSTM
Chapter 8 Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM
Chapter 9 Redundancy and Attention in Convolutional LSTM for Gesture Recognition
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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 Neighbor
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 forAction 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
3．4．1 Datasets
3．4．2 Training Details
3．4．3 Ablation Study
3．4．4 Comparison with the State-of-the-art Methods
3．4．5 Visualization of the Evolved Graph Topologies
3．5 Conclusion
Acknowledgements
References
Chapter 4 Graph-temporal LSTM Networks for Skeleton-based Action Recognition
4．1 Introduction
4．2 Related Work
4．3 GT-LSTM Networks
4．3．1 Pipeline Overview
4．3．2 Topology-learnable ST-GCN
4．3．3 GT-LSTM
4．3．4 GT-LSTM Networks
4．4 Experiments
4．4．1 Datasets
4．4．2 Training Details
4．4．3 Ablation Study
4．4．4 Comparison with the State-of-the-art Methods
4．5 Conclusion
References
Chapter 5 Spatio-temporal Interaction Graph Parsing Networks for Human-object Interaction Recognition
5．1 Introduction
5．2 Related Work
5．3 Overview
5．4 Proposed Approach
5．4．1 Video Feature Extraction
5．4．2 Spatio-temporal Interaction Graph Parsing
5．4．3 Inference
5．4．4 Implementation Details
5．5 Experiments
5．5．1 Dataset
5．5．2 Ablation Study
5．5．3 Comparison with the State-of-the-arts Methods
5．5．4 Visualization of Parsed Graphs
5．6 Conclusion
Acknowledgements
References
Chapter 6 Learning Spatio-temporal Features Using 3DCNN and Convolutional LSTM For Gesture Recognition
6．1 Introduction
6．2 Related Work
6．3 Method
6．3．1 2D Spatio-temporal Feature MapLearning
6．3．2 Classification Based on the 2D Feature Maps
6．3．3 Network Training
6．4 Experiments
6．4．1 Datasets
6．4．2 Implementation
6．4．3 Architecture Analysis
6．4．4 Comparison with the State-of-the-art Methods
6．5 Conclusion
Acknowledgements
References
……
Chapter 7 Multimodal Gesture Recognition Using 3D Convoluhon and Convolutional LSTM
Chapter 8 Continuous Gesture Segmentation and Recognition Using 3DCNN and Convolutional LSTM
Chapter 9 Redundancy and Attention in Convolutional LSTM for Gesture Recognition
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张亮，男，汉族，1981年5月生，西安电子科技大学教授，博士生导师，本硕博毕业于浙江大学，现任西安电子科技大学计算机科学与技术学院“嵌入式技术与视觉处理中心”主任，全国计算机学会嵌入式专委会委员，IEEE会员，ACM会员。主要研究方向为深度学习、手势手语识别、场景语义理解、嵌入式多核系统等，作为负责人先后承担国家重点研发计划、国家自然科学基金及企业合作项目多项。在IEEE TNNLS、IEEE TMI、IEEE TIP等本领域重要期刊和NeurIPS、CVPR、ICCV等国际会议上发表论文80余篇，授权专利20余项，获陕西省科技进步二等奖1项，陕西省高等学校科学技术奖一等奖1项。
李宁，男，满族，1990年8月生，中国船舶工业综合技术经济研究院工程师，本硕毕业于北京林业大学，国防科技大学博士生，现任中国船舶工业综合技术经济研究院人因工程中心主任助理、系统研发研究室主任，国家重点渠道人机工程标准化委员秘书，全国人类工效学会会员。主要研究方向为信息系统智能人机交互、人因工程设计与测评等。作为负责人先后承担国家重大型号科研、装发共性技术基础、科技委人机工效领域基金等多项工作。以作者在IEEEIOT、Soft computing等重点期刊和HFE、BigData等国际会议上发表论文20余篇，授权专利5项，获得中国船舶集团有限公司科技进步二等奖。
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李宁，男，满族，1990年8月生，中国船舶工业综合技术经济研究院工程师，本硕毕业于北京林业大学，国防科技大学博士生，现任中国船舶工业综合技术经济研究院人因工程中心主任助理、系统研发研究室主任，国家重点渠道人机工程标准化委员秘书，全国人类工效学会会员。主要研究方向为信息系统智能人机交互、人因工程设计与测评等。作为负责人先后承担国家重大型号科研、装发共性技术基础、科技委人机工效领域基金等多项工作。以作者在IEEEIOT、Soft computing等重点期刊和HFE、BigData等国际会议上发表论文20余篇，授权专利5项，获得中国船舶集团有限公司科技进步二等奖。
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This book provides a series of gesture and behavior recognition methods based on multimodal data representation. The data modalities include image data and skeleton data， and the modeling methods include traditional codebook， topological graph， and LSTM architectures. The tasks include single gesture recognition classification， single action recognition classification， continuous gesture classification， complex behavior classification of human interaction and other tasks of different complexity. This book focuses on the data processing methods of each modality， and the modeling methods for different tasks. We hope the reader can leam basic gesture and action recognition methods from this book， and develop a model system that suits their needs on this basis.This book can be used as a textbook for graduate， postgraduate and PhD students majoring in computer science， automation， etc. It can also be used as a reference for the reader who is interested in gesture recognition， human action interaction， sequence data processing， and deep neural network design， and who hopes to contribute to the fields.
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