Tái định danh trong hệ thống camera giám sát tự động

MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN THUY BINH PERSON RE-IDENTIFICATION IN A SURVEILLANCE CAMERA NETWORK DOCTORAL DISSERTATION OF ELECTRONICS ENGINEERING Hanoi−2020 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN THUY BINH PERSON RE-IDENTIFICATION IN A SURVEILLANCE CAMERA NETWORK Major: Electronics Engineering Code: 9520203 DOCTORAL DISSERTATION OF ELECTRONICS ENGINEERING SUPERVISORS: 1.Assoc Prof Pham Ngoc Nam 2.Assoc Prof Le Thi Lan Hanoi−2020 DECLARATION OF AUTHORSHIP I, Nguyen Thuy Binh, declare that the thesis titled "Person re-identification in a surveillance camera network" 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 be submitted for any other degree or qualifications at Hanoi University of Science and Technology or any other institutions Appropriate acknowledge has been given within this thesis where reference has been made to the published work of others The thesis submitted is my own, except where work in the collaboration has been included The collaborative contributions have been clearly indicated Hanoi, 24/11/ 2020 PhD Student SUPERVISORS i ACKNOWLEDGEMENT This dissertation was written during my doctoral course at School of Electronics and Telecommunications (SET) and International Research Institute of Multimedia, Information, Communication and Applications (MICA), Hanoi University of Science and Technology (HUST) I am so grateful for all people who always support and encourage me for completing this study First, I would like to express my sincere gratitude to my advisors Assoc Prof Pham Ngoc Nam and Assoc Prof Le Thi Lan for their effective guidance, their patience, continuous support and encouragement, and their immense knowledge I would like to express my gratitude to Dr Vo Le Cuong and Dr Ha thi Thu Lan for their help I would like to thank to all member of School of Electronics and Telecommunications, International Research Institute of Multimedia, Information, Communications and Applications (MICA), Hanoi University of Science and Technology (HUST) as well as all of my colleagues in Faculty of Electrical-Electronic Engineering, University of Transport and Communications (UTC) They have always helped me on research process and given helpful advises for me to overcome my own difficulties Moreover, the attention at scientific conferences has always been a great experience for me to receive many the useful comments During my PhD course, I have received many supports from the Management Board of School of Electronics and Telecommunications, MICA Institute, and Faculty of Electrical-Electronic Engineering My sincere thank to Assoc Prof Nguyen Huu Thanh, Dr Nguyen Viet Son and Assoc Prof Nguyen Thanh Hai who gave me a lot of support and help Without their precious support, it has been impossible to conduct this research Thanks to my employer, University of Transport and Communications (UTC) for all necessary support and encouragement during my PhD journey I am also grateful to Vietnam’s Program 911, HUST and UTC projects for their generous financial support Special thanks to my family and relatives, particularly, my beloved husband and our children, for their never-ending support and sacrifice Hanoi, 2020 Ph.D Student ii CONTENTS DECLARATION OF AUTHORSHIP i ACKNOWLEDGEMENT ii CONTENTS vi SYMBOLS vi LIST OF TABLES x LIST OF FIGURES xiv INTRODUCTION CHAPTER LITERATURE REVIEW 1.1 Person ReID classifications 1.1.1 Single-shot versus Multi-shot 1.1.2 Closed-set versus Open-set person ReID 1.1.3 Supervised and unsupervised person ReID 10 1.2 Datasets and evaluation metrics 11 1.2.1 Datasets 11 1.2.2 Evaluation metrics 16 1.3 Feature extraction 16 1.3.1 Hand-designed features 17 1.3.2 Deep-learned features 20 1.4 Metric learning and person matching 25 1.4.1 Metric learning 25 1.4.2 Person matching 28 1.5 Fusion schemes for person ReID 29 1.6 Representative frame selection 31 1.7 Fully automated person ReID systems 33 1.8 Research on person ReID in Vietnam 34 CHAPTER MULTI-SHOT PERSON RE-ID THROUGH REPRESENTATIVE FRAMES SELECTION AND TEMPORAL FEATURE POOLING 36 2.1 Introduction 36 2.2 Proposed method 36 2.2.1 Overall framework 2.2.2 Representative image selection 36 37 iii 2.2.3 Image-level feature extraction 44 2.2.4 Temporal feature pooling 49 2.2.5 Person matching 50 2.3 Experimental results 55 2.3.1 Evaluation of representative frame extraction and temporal feature pooling schemes 55 2.3.2 Quantitative evaluation of the trade-off between the accuracy and computational time 61 2.3.3 Comparison with state-of-the-art methods 63 2.4 Conclusions and Future work 65 CHAPTER PERSON RE-ID PERFORMANCE IMPROVEMENT BASED ON FUSION SCHEMES 67 3.1 Introduction 67 3.2 Fusion schemes for the first setting of person ReID 3.2.1 Image-to-images person ReID 69 69 3.2.2 Images-to-images person ReID 75 3.2.3 Obtained results on the first setting 76 3.3 Fusion schemes for the second setting of person ReID 3.3.1 The proposed method 82 82 3.3.2 Obtained results on the second setting 86 3.4 Conclusions 89 CHAPTER QUANTITATIVE EVALUATION OF AN END-TO-END PERSON REID PIPELINE 91 4.1 Introduction 91 4.2 An end-to-end person ReID pipeline 92 4.2.1 Pedestrian detection 4.2.2 Pedestrian tracking 92 97 4.2.3 Person ReID 98 4.3 GOG descriptor re-implementation 99 4.3.1 Comparison the performance of two implementations 4.3.2 Analyze the effect of GOG parameters 99 99 4.4 Evaluation performance of an end-to-end person ReID pipeline 101 4.4.1 The effect of human detection and segmentation on person ReID in singleshot scenario 102 iv 4.4.2 The effect of human detection and segmentation on person ReID in multishot scenario 104 4.5 Conclusions and Future work 107 PUBLICATIONS 112 Bibliography 113 v ABBREVIATIONS No Abbreviation Meaning ACF Aggregate Channel Features AIT Austrian Institute of Technology AMOC Accumulative Motion Context BOW Bag of Words CAR Learning Compact Appearance Representation CIE The International Commission on Illumination CFFM Comprehensive Feature Fusion Mechanism CMC Cummulative Matching Characteristic CNN Convolutional Neural Network 10 CPM Convolutional Pose Machines 11 CVPDL Cross-view Projective Dictionary Learning 12 CVPR Conference on Computer Vision and Pattern Recognition 13 DDLM Discriminative Dictionary Learning Method 14 DDN Deep Decompositional Network 15 DeepSORT Deep learning Simple Online and Realtime Tracking 16 DFGP Deep Feature Guided Pooling 17 DGM Dynamic Graph Matching 18 DPM Deformable Part-Based Model 19 ECCV European Conference on Computer Vision 20 FAST 3D Fast Adaptive Spatio-Temporal 3D 21 FEP Flow Energy Profile 22 FNN Feature Fusion Network 23 FPNN Filter Pairing Neural Network 24 GOG Gaussian of Gaussian 25 GRU Gated Recurrent Unit 26 HOG Histogram of Oriented Gradients 27 HUST Hanoi University of Science and Technology 28 IBP Indian Buffet Process 29 ICCV International Conference on Computer Vision 30 ICIP International Conference on Image Processing vi 31 IDE ID-Discriminative Embedding 32 iLIDS-VID Imagery Library for Intelligent Detection Systems 33 ILSVRC ImageNet Large Scale Visual Recognition Competition 34 ISR TIterative Spare Ranking 35 KCF Kernelized Correlation Filter 36 KDES Kenel DEScriptor 37 KISSME Keep It Simple and Straightforward MEtric 38 kNN k-Nearest Neighbour 39 KXQDA Kernel Cross-view Quadratic Discriminative Analysis 40 LADF Locally-Adaptive Decision Functions 41 LBP Local Binary Pattern 42 LDA LinearDiscriminantAnalysis 43 LDFV Local Descriptor and coded by Feature Vector 44 LMNN Large Margin Nearest Neighbor 45 LMNN-R Large Margin Nearest Neighbor with Rejection 46 LOMO LOcal Maximal Occurrence 47 LSTM Long-Short Term Memory 48 LSTMC Long Short-Term Memory network with a Coupled gate 49 mAP mean Average Precision 50 MAPR Multimedia Analysis and Pattern Recognition 51 Mask R-CNN Mask Region with CNN 52 MCT Multi -Camera Tracking 53 MCCNN Multi-Channel CNN 54 MCML Maximally Collapsing Metric Learning 55 MGCAM Mask-Guided Contrastive Attention Model 56 ML Machine Learning 57 MLAPG Metric Learning by Accelerated Proximal Gradient 58 MLR Metric Learning to Rank 59 MOT Multiple Object Tracking 60 MSCR Maximal Stable Color Region 61 MSVF Maximally Stable Video Frame 62 MTMCT Multi-Target Multi-Camera Tracking 63 Person ReID Person Re -Identification 64 Pedparsing Pedestrian Parsing 65 PPN Pose Prediction Network vii 66 PRW Person Re-identification in the Wild 67 QDA Quadratic Discriminative Analysis 68 RAiD Re-Identification Across indoor-outdoor Dataset 69 RAP Richly Annotated Pedestrian 70 ResNet Residual Neural Network 71 RHSP Recurrent High-Structured Patches 72 RKHS Reproducing Kernel Hilbert Space 73 RNN Recurrent Neural Network 74 ROIs Region of Interests 75 SDALF Symmetry Driven Accumulation of Local Feature 76 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Figure 1.3a) the person appears on both cameras, while she appears only on the camera- A in Figure 1.3b) Camera- A Camera- B Camera- A (a) Close-set person ReID Camera- B (b) Open-set person ReID Figure... static non-overlapping cameras These images suffer from large variations in illuminations, view-point, poses, etc Figure 1.5 shows camera layout for PRID-2011 dataset, two cameras are installed... out due to strong occlusions, sudden disappearance/appearance or number of reliable images for each person in each camera view less than five After filtering, there are 385 persons in camera view