Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.

Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.Nghiên cứu các kỹ thuật học sâu trong biểu diễn và nhận dạng hoạt động của người từ dữ liệu khung xương.

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY PHAM DINH TAN A STUDY ON DEEP LEARNING TECHNIQUES FOR HUMAN ACTION REPRESENTATION AND RECOGNITION WITH SKELETON DATA DOCTORAL DISSERTATION IN COMPUTER ENGINEERING Hanoi−2022 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY PHAM DINH TAN A STUDY ON DEEP LEARNING TECHNIQUES FOR HUMAN ACTION REPRESENTATION AND RECOGNITION WITH SKELETON DATA Major: Computer Engineering Code: 9480106 DOCTORAL DISSERTATION IN COMPUTER ENGINEERING SUPERVISORS: Assoc Prof Vu Hai Assoc Prof Le Thi Lan Hanoi−2022 DECLARATION OF AUTHORSHIP I, Pham Dinh Tan, declare that the dissertation titled "A study on deep learning techniques for human action representation and recognition with skeleton data" has been entirely composed by myself I assure some points as follows: This work was done wholly or mainly while in candidature for a Ph.D research degree at Hanoi University of Science and Technology The work has not been submitted for any other degree or qualifications at Hanoi University of Science and Technology or any other institution Appropriate acknowledgment has been given within this dissertation, where reference has been made to the published work of others The dissertation submitted is my own, except where work in the collaboration has been included The collaborative contributions have been indicated Hanoi, March 08, 2022 Ph.D Student SUPERVISORS Assoc Prof Vu Hai Assoc Prof Le Thi Lan i ACKNOWLEDGEMENT This dissertation is composed during my Ph.D at the Computer Vision Department, MICA Institute, Hanoi University of Science and Technology I am grateful to all people who contribute in different ways to my Ph.D journey First, I would like to express sincere thanks to my supervisors Assoc Prof Vu Hai and Assoc Prof Le Thi Lan for their guidance and support I would like to thank all MICA members for their help during my Ph.D study My sincere thank to Dr Nguyen Viet Son, Assoc Prof Dao Trung Kien, and Assoc Prof Tran Thi Thanh Hai for giving me a lot of support and valuable advice Many thanks to Dr Nguyen Thuy Binh, Nguyen Hong Quan, Hoang Van Nam, Nguyen Tien Nam, and Pham Quang Tien for their support I would like to thank colleagues at Hanoi University of Mining and Geology for all support during my Ph.D study Special thanks to my family for understanding my hours glued to the computer screen Hanoi, March 08, 2022 Ph.D Student ii ABSTRACT Human action recognition (HAR) from color and depth sensors (RGB-D), especially derived information such as skeleton data, is receiving the research community’s attention due to its wide range of applications HAR has many practical applications such as abnormal event detection from camera surveillance, gaming, human-machine interaction, elderly monitoring, and virtual/augmented reality In addition to the advantages in fast computation, low storage, and immutability with human appearance, skeleton data have shortcomings The shortcomings include pose estimation errors, skeleton noise in complex actions, and incompleteness due to occlusion Moreover, action recognition remains challenging due to the diversity of human actions, intraclass variations, and inter-class similarities The dissertation focuses on methods to improve the performances of action recognition using the skeleton data The proposed methods are evaluated using public skeleton datasets collected by RGB-D sensors Especially, they consist of MSR-Action3D/MICA-Action3D - datasets with high-quality skeleton data, CMDFALL - a challenging dataset with noise in skeleton data, and NTU RGB+D - a worldwide benchmark among the large-scale datasets Therefore, these datasets cover different dataset scales as well as the quality of skeleton data To overcome the limitations of the skeleton data, the dissertation presents techniques in different approaches First, as joints have different levels of engagement in each action, techniques for selecting joints that play an important role in human actions are proposed, including both Preset joint subset selection and automatic joint subset selection Two frameworks are evaluated to show the performance of using a subset of joints for action representation The first framework employs Dynamic Time Warping (DTW) and Fourier Temporal Pyramid (FTP), while the second one applies Covariance Descriptors extracted on both joint position and joint velocity Experimental results show that joint subsect selection helps improve action recognition performance on datasets with noise in skeleton data However, HAR based on hand-designed features could not exploit the inherent graph structure of the human skeleton Recent Graph Convolution Networks (GCNs) are studied to handle these issues Among GCN models, Attention-enhanced Adaptive Convolutional Network (AAGCN) is used as the baseline model AAGCN achieves state-of-the-art performance on large-scale datasets such as NTU-RGBD and Kinetics However, AAGCN employs only joint information Therefore, a Feature Fusion (FF) module is proposed in this dissertation The new model is named FF-AAGCN The performance of FF-AAGCN is evaluated on the large-scale dataset NTU-RGBD and CMDFALL The evaluation results show that the proposed method is robust to noise iii and invariant to the skeleton translation Particularly, FF-AAGCN achieves remarkable results on challenging datasets Finally, as the computing capacity of edge devices is limited, a lightweight deep learning model is expected for application deployment A lightweight GCN architecture is proposed to show that the complexity of GCN architecture can still be reduced depending on the dataset’s characteristics The proposed lightweight model is suitable for application development on edge devices Hanoi, March 08, 2022 Ph.D Student iv CONTENTS DECLARATION OF AUTHORSHIP i ACKNOWLEDGEMENT ii ABSTRACT iii CONTENTS viii ABBREVIATIONS viii SYMBOLS x LIST OF TABLES xiii LIST OF FIGURES xvi INTRODUCTION CHAPTER LITERATURE REVIEW 1.1 Introduction 1.2 An overview on action recognition 1.3 Data modalities for action recognition 1.3.1 Color data 10 1.3.2 Depth data 10 1.3.3 Skeleton data 1.3.4 Other modalities 11 11 1.3.5 Multi-modality 13 1.4 Skeleton data collection 1.4.1 Data collection from motion capture systems 13 13 1.4.2 Data collection from RGB+D sensors 14 1.4.3 Data collection from pose estimation 15 1.5 Benchmark datasets 17 1.5.1 MSR-Action3D 18 1.5.2 MICA-Action3D 19 1.5.3 CMDFALL 1.5.4 NTU RGB+D 19 19 1.6 Skeleton-based action recognition methods 21 1.6.1 Handcraft-based methods 21 1.6.1.1 Joint-based action recognition 1.6.1.2 Body part-based action recognition 22 24 v 1.6.2 Deep learning-based methods 27 1.6.2.1 Convolutional Neural Networks 28 1.6.2.2 Recurrent Neural Networks 29 1.7 Research on action recognition in Vietnam 32 1.8 Conclusion of the chapter 34 CHAPTER JOINT SUBSET SELECTION FOR SKELETON-BASED HUMAN ACTION RECOGNITION 35 2.1 Proposed methods 2.1.1 Preset Joint Subset Selection 36 36 2.1.1.1 Spatial-Temporal Representation 38 2.1.1.2 Dynamic Time Warping 38 2.1.1.3 Fourier Temporal Pyramid 2.1.2 Automatic Joint Subset Selection 39 39 2.1.2.1 Joint weight assignment 40 2.1.2.2 Most informative joint selection 41 2.1.2.3 Human action recognition based on MIJ joints 41 2.2 Experimental results 44 2.2.1 Evaluation metrics 44 2.2.2 Preset Joint Subset Selection 2.2.3 Automatic Joint Subset Selection 45 46 2.3 Conclusion of the chapter 55 CHAPTER FEATURE FUSION FOR THE GRAPH CONVOLUTIONAL NETWORK 56 3.1 Introduction 56 3.2 Related work on Graph Convolutional Networks 56 3.3 Proposed method 63 3.4 Experimental results 68 3.5 Discussion 77 3.6 Conclusion of the chapter 81 CHAPTER THE PROPOSED LIGHTWEIGHT GRAPH CONVOLUTIONAL NETWORK 82 4.1 Introduction 82 4.2 Related work on Lightweight Graph Convolutional Networks 82 4.3 Proposed method 84 4.4 Experimental results 86 vi 4.5 Application demonstration 95 4.6 Conclusion of the chapter 97 CONCLUSION AND FUTURE WORKS 99 PUBLICATIONS 101 BIBLIOGRAPHY 102 vii ABBREVIATIONS No Abbreviation Meaning 2D Two-Dimensional 3D Three-Dimensional AAGCN Attention-enhanced Adaptive Graph Convolutional Network AMIJ Adaptive number of Most Informative Joints AGCN Adaptive Graph Convolutional Network AS Action Set AS-GCN Actional-Structural Graph Convolutional Network BN Batch Normalization BPL Body Part Location 10 CAM Channel Attention Module 11 CCTV Close-Circuit Television 12 CNN Convolutional Neural Network 13 CovMIJ Covariance Descriptor on Most Informative Joints 14 CPU Central Processing Unit 15 CS Cross-Subject 16 CV Cross-View 17 DFT Discrete Fourier Transform 18 DTW Dynamic Time Warping 19 FC Fully Connected 20 FF Feature Fusion 21 FLOP Floating Point OPeration 22 FMIJ Fixed number of Most Informative Joints 23 fps f rames per second 24 FTP Fourier Temporal Pyramid 25 GCN Graph Convolutional Network 26 GCNN Graph-based Convolutional Neural Network 27 GPU Graphical Processing Unit 28 GRU Gated Recurrent Unit 29 HAR Human Action Recognition 30 HCI Human-Computer Interaction viii connecting symmetrical joints achieves excellent performance on CMDFALL with fewer model parameters and FLOPs The number of parameters is reduced using Preset JSS and layer pruning Experimental results show that the lightweight model with graph type B (JSS-B) outperforms the baseline AAGCN on challenging datasets with trainable parameters 5.6 times fewer than the baseline The computation complexity in FLOPs of the proposed model is 3.5 times lower than that of the baseline on CMDFALL A study is conducted to evaluate the performance of LW-FF-AAGCN with different dataset sizes LW-FF-AAGCN is an efficient deep learning model for HAR implementation in real-world applications A demo is presented using the proposed method for human action recognition Results in this chapter have been submitted to the Multimedia Tools and Applications (MTAP) journal in the paper "A Lightweight Graph Convolutional Network for Skeleton-based Action Recognition" 98 CONCLUSION AND FUTURE WORKS Conclusions Noise in the skeleton data can degrade the performance of action recognition Joint subset selection (JSS), feature combining, and the use of graph-based deep learning networks are proposed to improve representation efficiency and recognition performance It is found in the first contribution of the dissertation is that joint subset selection with both preset configuration and automatic schemes help improve the performance of action recognition In the second contribution, a Feature Fusion module is coupled with AAGCN to form FF-AAGCN The Feature Fusion is a simple and efficient data pre-processing module for graph-based deep learning, especially for noisy skeleton data The proposed method FF-AAGCN outperforms the baseline AAGCN on CMDFALL, a challenging dataset with noise in skeleton data On the large-scale dataset NTU RGB+D, FF-AAGCN obtains competitive results compared to AAGCN The third contribution is a lightweight model LW-FF-AAGCN The number of model parameters in LW-FF-AAGCN is 5.6 times less than the baseline The proposed lightweight model is suitable for application development for edge devices with limited computation capacity LW-FF-AAGCN outperforms both AAGCN and FF-AAGCN on CMDFALL A trade-off in the performance of the lightweight model is observed on the large-scale dataset NTU RGB+D These results suggest directions for future research in both short-term and long-term perspectives Future work Short-Term Perspectives • Study on noise in the skeleton data caused by pose estimation errors using RGB-D sensors Standard calibrated Mocap system is required for evaluation • Study different statistical metrics for Joint Subset Selection, such as the variance of joint angles Other JSS methods should be implemented on graph-based deep learning networks • Develop graph-based lightweight models for application development on edge devices As computation capacity is limited on edge devices, lightweight models are required for real-time applications Further research will aim at designing lightweight models from the garden of deep learning models in the literature • Study on the interpretability of action recognition using graph-based deep learning Deep learning approaches, which are prevalent in the present literature, have 99 excellent performance at the expense of the learning process’s understandability Handcrafted learning can be deemed less generalizable and more data-type specific in general They are, however, more intelligible from a human standpoint The optimum trade-off strategy is still an open question [24] • Improve the quality of pose estimation for high-performance action recognition Long-Term Perspectives • Extend the proposed methods to continuous skeleton-based human action recognition • Extend the study of Graph Convolutional Networks to Geometric Deep Learning There is a garden of deep learning models in the literature, including CNNs, RNNs, GCNs, and many others This leads to a requirement to construct a general mathematical framework for all these models Geometric Deep Learning is such an approach to unify these deep learning models by exploring the common mathematics in these models • Develop applications using the proposed models for human action recognition such as elderly remote monitoring in healthcare or camera surveillance for abnormal behavior detection 100 PUBLICATIONS Conferences [C1] Tien-Nam Nguyen, Dinh-Tan Pham, Thi-Lan Le, Hai Vu, and Thanh-Hai Tran (2018), Novel Skeleton-based Action Recognition Using Covariance Descriptors on Most Informative Joints, Proceedings of International Conference on Knowledge and Systems Engineering (KSE 2018), IEEE, Vietnam, ISBN: 978-1-5386-6113-0, pp.50-55, 2018 [C2] Dinh-Tan Pham, Tien-Nam Nguyen, Thi-Lan Le, and Hai Vu (2019), Analyzing Role of Joint Subset Selection in Human Action Recognition, Proceedings of NAFOSTED Conference on Information and Computer Science (NICS 2019), IEEE, Vietnam, ISBN: 978-1-7281-5163-2, pp.61-66, 2019 [C3] Dinh-Tan Pham, Tien-Nam Nguyen, Thi-Lan Le, and Hai Vu (2020), SpatioTemporal Representation for Skeleton-based Human Action Recognition, Proceedings of International Conference on Multimedia Analysis and Pattern Recognition (MAPR 2020), IEEE, Vietnam, ISBN: 978-1-7281-6555-4, pp.1-6, 2020 Journals [J1] Dinh-Tan Pham, Quang-Tien Pham, Thi-Lan Le, and Hai Vu (2021), An Efficient Feature Fusion of Graph Convolutional Networks and its application for RealTime Traffic Control Gestures Recognition, IEEE Access, ISSN: 2169-3536, pp.121930 - 121943, 2021 (ISI, Q1) [J2] Van-Toi Nguyen, Tien-Nam Nguyen, Thi-Lan Le, Dinh-Tan Pham, and Hai Vu (2020), Adaptive most joint selection and covariance descriptions for a robust skeleton-based human action recognition, Multimedia Tools and Applications (MTAP), Springer, DOI: 10.1007/s11042-021-10866-4, pp.1-27, 2021 (ISI, Q1) [J3] Dinh Tan Pham, Thi Phuong Dang, Duc Quang Nguyen, Thi Lan 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Kien, and Assoc Prof Tran Thi Thanh Hai for giving me a lot of support and valuable advice Many thanks to Dr Nguyen Thuy Binh, Nguyen Hong Quan, Hoang Van Nam, Nguyen Tien Nam, and Pham Quang Tien... can act like humans Robots can be employed in any circumstance and for any purpose Many robots are now working in hazardous locations, manufacturing processes, and other situations where humans... can be found in [5–9] The demand for systems that can understand human actions and alert patients about potential physical and mental health concerns is rapidly increasing Medical experts can