Person re - Identification in a surveillance camera network

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, Infor-
mation, 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 Telecom-
munications, International Research Institute of Multimedia, Information, Communi-
cations 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
CHAPTER 1. LITERATURE REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.1. Person ReID classifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.1.1. Single-shot versus Multi-shot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.1.2. Closed-set versus Open-set person ReID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
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 2. MULTI-SHOT PERSON RE-ID THROUGH REPRESEN-
TATIVE FRAMES SELECTION AND TEMPORAL FEATURE POOLING
36
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2. Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.1. Overall framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.2. Representative image selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 compu-
tational time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
61
2.3.3. Comparison with state-of-the-art methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.4. Conclusions and Future work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
CHAPTER 3. 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 . . . . . . . . . . . . . . . . . . . . . . . 69
3.2.1. Image-to-images person ReID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . 82
3.3.1. The proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.3.2. Obtained results on the second setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
CHAPTER 4. 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2.2. Pedestrian tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.2.3. Person ReID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3. GOG descriptor re-implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.3.1. Comparison the performance of two implementations . . . . . . . . . . . . . . . . . 99
4.3.2. Analyze the effect of GOG parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 single-
shot scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
102
iv
4.4.2. The effect of human detection and segmentation on person ReID in multi-
shot scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
104
4.5. Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
PUBLICATIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
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ABBREVIATIONS
No. Abbreviation Meaning
1 ACF Aggregate Channel Features
2 AIT Austrian Institute of Technology
3 AMOC Accumulative Motion Context
4 BOW Bag of Words
5 CAR Learning Compact Appearance Representation
6 CIE The International Commission on Illumination
7 CFFM Comprehensive Feature Fusion Mechanism
8 CMC Cummulative Matching Characteristic
9 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 SCNCD Salient Color Names bas ...  pp. 213–216. IEEE.
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