3-D object detections and recognitions: assisting visually impaired people

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
LE VAN HUNG
3-D OBJECT DETECTIONS AND
RECOGNITIONS: ASSISTING VISUALLY
IMPAIRED PEOPLE
Major: Computer Science
Code: 9480101
DOCTORAL DISSERTATION OF
COMPUTER SCIENCE
SUPERVISORS:
1. Dr. Vu Hai
2. Assoc. Prof. Dr. Nguyen Thi Thuy
Hanoi  2018
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY
LE VAN HUNG
3-D OBJECT DETECTIONS AND
RECOGNITIONS: ASSISTING VISUALLY
IMPAIRED PEOPLE
Major: Computer Science
Code: 9480101
DOCTORAL DISSERTATION OF
COMPUTER SCIENCE
SUPERVISORS:
1. Dr. Vu Hai
2. Assoc. Prof. Dr. Nguyen Thi Thuy
Hanoi  2018
DECLARATION OF AUTHORSHIP
I, Le Van Hung, declare that this dissertation titled, "3-D Object Detections and
Recognitions: Assisting Visually Impaired People in Daily Activities ", and the works
presented in it are my own. I conrm that:
 This work was done wholly or mainly while in candidature for a Ph.D. research
degree at Hanoi University of Science and Technology.
 Where any part of this thesis has previously been submitted for a degree or any
other qualication at Hanoi University of Science and Technology or any other
institution, this has been clearly stated.
 Where I have consulted the published work of others, this is always clearly at-
tributed.
 Where I have quoted from the work of others, the source is always given. With
the exception of such quotations, this dissertation is entirely my own work.
 I have acknowledged all main sources of help.
 Where the dissertation is based on work done by myself jointly with others, I
have made exactly what was done by others and what I have contributed myself.
Hanoi, November 2018
PhD Student
Le Van Hung
SUPERVISORS
Dr. Vu Hai Assoc. Prof. Dr. Nguyen Thi Thuy
i
ACKNOWLEDGEMENT
This dissertation was written during my doctoral course at International Research
Institute Multimedia, Information, Communication and Applications (MICA), Hanoi
University of Science and Technology (HUST). It is my great pleasure to thank all the
people who supported me for completing this work.
First, I would like to express my sincere gratitude to my advisors Dr. Hai Vu
and Assoc. Prof. Dr. Thi Thuy Nguyen for their continuous support, their patience,
motivation, and immense knowledge. Their guidance helped me all the time of research
and writing this dissertation. I could not imagine a better advisor and mentor for my
Ph.D. study.
Besides my advisors, I would like to thank to Assoc. Prof. Dr. Thi-Lan Le,
Assoc. Prof. Dr. Thanh-Hai Tran and members of Computer Vision Department at
MICA Institute. The colleagues have assisted me a lot in my research process as well
as they are co-authored in the published papers. Moreover, the attention at scientic
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 MICA Institute. My sincere thank to Prof. Yen Ngoc Pham, Prof. Eric
Castelli and Dr. Son Viet Nguyen, who gave me the opportunity to join research
works, and gave me permission to joint to the laboratory in MICA Institute. Without
their precious support, it has been being impossible to conduct this research.
As a Ph.D. student of 911 program, I would like to thank this programme for
nancial support. I also gratefully acknowledge the nancial support for attending
the conferences from Nafosted-FWO project (FWO.102.2013.08) and VLIR project
(ZEIN2012RIP19). I would like to thank the College of Statistics over the years both
at my career work and outside of the work.
Special thanks to my family, particularly, to my mother and father for all of their
sacrices that they have made on my behalf. I also would like to thank my beloved
wife for everything she supported me.
Hanoi, November 2018
Ph.D. Student
Le Van Hung
ii
CONTENTS
DECLARATION OF AUTHORSHIP i
ACKNOWLEDGEMENT ii
CONTENTS v
SYMBOLS vi
LIST OF TABLES viii
LIST OF FIGURES xvii
1 LITERATURE REVIEW 8
1.1 Aided-systems for supporting visually impaired people . . . . . . . . . 8
1.1.1 Aided-systems for navigation services . . . . . . . . . . . . . . . 8
1.1.2 Aided-systems for obstacle detection . . . . . . . . . . . . . . . 9
1.1.3 Aided-systems for locating the interested objects in scenes . . . 11
1.1.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2 3-D object detection, recognition from a point cloud data . . . . . . . . 13
1.2.1 Appearance-based methods . . . . . . . . . . . . . . . . . . . . 13
1.2.1.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.2.2 Geometry-based methods . . . . . . . . . . . . . . . . . . . . . . 16
1.2.3 Datasets for 3-D object recognition . . . . . . . . . . . . . . . . 17
1.2.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3 Fitting primitive shapes . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.3.1 Linear tting algorithms . . . . . . . . . . . . . . . . . . . . . . 18
1.3.2 Robust estimation algorithms . . . . . . . . . . . . . . . . . . . 19
1.3.3 RANdom SAmple Consensus (RANSAC) and its variations . . . 20
1.3.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 POINT CLOUD REPRESENTATION AND THE PROPOSED METHOD
FOR TABLE PLANE DETECTION 24
2.1 Point cloud representations . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1.1 Capturing data by a Microsoft Kinect sensor . . . . . . . . . . . 24
2.1.2 Point cloud representation . . . . . . . . . . . . . . . . . . . . . 25
2.2 The proposed method for table plane detection . . . . . . . . . . . . . 28
2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
iii
2.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.3 The proposed method . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.3.1 The proposed framework . . . . . . . . . . . . . . . . . 30
2.2.3.2 Plane segmentation . . . . . . . . . . . . . . . . . . . . 32
2.2.3.3 Table plane detection and extraction . . . . . . . . . . 34
2.2.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.4.1 Experimental setup and dataset collection . . . . . . . 36
2.2.4.2 Table plane detection evaluation method . . . . . . . . 37
2.2.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.3 Separating the interested objects on the table plane . . . . . . . . . . . 46
2.3.1 Coordinate system transformation . . . . . . . . . . . . . . . . . 46
2.3.2 Separating table plane and the interested objects . . . . . . . . 48
2.3.3 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3 PRIMITIVE SHAPES ESTIMATION BY A NEW ROBUST ES-
TIMATOR USING GEOMETRICAL CONSTRAINTS 51
3.1 Fitting primitive shapes by GCSAC . . . . . . . . . . . . . . . . . . . . 52
3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.1.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.3 The proposed a new robust estimator . . . . . . . . . . . . . . . 55
3.1.3.1 Overview of the proposed robust estimator (GCSAC) . 55
3.1.3.2 Geometrical analyses and constraints for qualifying good
samples . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.4 Experimental results of robust estimator . . . . . . . . . . . . . 64
3.1.4.1 Evaluation datasets of robust estimator . . . . . . . . 64
3.1.4.2 Evaluation measurements of robust estimator . . . . . 67
3.1.4.3 Evaluation results of a new robust estimator . . . . . . 68
3.1.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.2 Fitting objects using the context and geometrical constraints . . . . . . 76
3.2.1 The proposed method of nding objects using the context and
geometrical constraints . . . . . . . . . . . . . . . . . . . . . . . 77
3.2.1.1 Model verication using contextual constraints . . . . 77
3.2.2 Experimental results of nding objects using the context and
geometrical constraints . . . . . . . . . . . . . . . . . . . . . . . 78
3.2.2.1 Descriptions of the datasets for evaluation . . . . . . . 78
3.2.2.2 Evaluation measurements . . . . . . . . . . . . . . . . 81
3.2.2.3 Results of nding objects using the context and geo-
metrical constraints . . . . . . . . . . . . . . . . . . . 82
3.2.3 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
iv
4 DETECTION AND ESTIMATION OF A 3-D OBJECT MODEL
FOR A REAL APPLICATION 86
4.1 A Comparative study on 3-D object detection . . . . . . . . . . . . . . 86
4.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1.3 Three dierent approaches for 3-D objects detection in a complex
scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.1.3.1 Geometry-based method for Primitive Shape detection
Method (PSM) . . . . . . . . . . . . . . . . . . . . . 90
4.1.3.2 Combination of Clustering objects and Viewpoint Features
Histogram, GCSAC for estimating 3-D full object mod-
els (CVFGS) . . . . . . . . . . . . . . . . . . . . . . . 91
4.1.3.3 Combination of Deep Learning based and GCSAC for
estimating 3-D full object models (DLGS) . . . . . . . 93
4.1.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.1.4.1 Data collection . . . . . . . . . . . . . . . . . . . . . . 95
4.1.4.2 Evaluation method . . . . . . . . . . . . . . . . . . . . 98
4.1.4.3 Setup parameters in the evaluations . . . . . . . . . . 101
4.1.4.4 Evaluation results . . . . . . . . . . . . . . . . . . . . 102
4.1.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.2 Deploying an aided-system for visually impaired people . . . . . . . . . 109
4.2.1 Environment and material setup for the evaluation . . . . . . . 111
4.2.2 Pre-built script . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.2.3 Performances of the real system . . . . . . . . . . . . . . . . . . 114
4.2.3.1 Evaluation of nding 3-D objects . . . . . . . . . . . . 115
4.2.4 Evaluation of usability and discussion . . . . . . . . . . . . . . . 118
5 CONCLUSION AND FUTURE WORKS 121
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Bibliography 125
PUBLICATIONS 139
v
ABBREVIATIONS
No. Abbreviation Meaning
1 API Application Programming Interface
2 CNN Convolution Neural Network
2 CPU Central Processing Unit
3 CVFH Clustered Viewpoint Feature Histogram
4 FN False Negative
5 FP False Positive
6 FPFH Fast Point Feature Histogram
7 fps frame per second
8 GCSAC Geometrical Constraint SAmple Consensus
9 GPS Global Positioning System
10 GT Ground Truth
11 HT Hough Transform
12 ICP Iterative Closest Point
13 ISS Intrinsic Shape Signatures
14 JI Jaccard Index
15 KDES Kernel DEScriptors
16 KNN K Nearest Neighbors
17 LBP Local Binary Patterns
18 LMNN Large Margin Nearest Neighbor
19 LMS Least Mean of Squares
20 LO-RANSAC Locally Optimized RANSAC
21 LRF Local Receptive Fields
22 LSM Least Squares Method
23 MAPSAC Maximum A Posteriori SAmple Consensus
24 MLESAC Maximum Likelihood Estimation SAmple Consensus
25 MS MicroSoft
26 MSAC M-estimator SAmple Consensus
27 MSI Modied Plessey
28 MSS Minimal Sample Set
29 NAPSAC N-Adjacent Points SAmple Consensus
vi
30 NARF Normal Aligned Radial Features
31 NN Nearest Neighbor
32 NNDR Nearest Neighbor Distance Ratio
33 OCR Optical Character Recognition
34 OPENCV OPEN source Computer Vision Library
35 PC Persional Computer
36 PCA Principal Component Analysis
37 PCL Point Cloud Library
38 PROSAC PROgressive SAmple Consensus
39 QR code Quick Response Code
40 RAM Random Acess Memory
41 RANSAC RANdom SAmple Consensus
42 RFID Radio-Frequency IDentication
43 R-RANSAC Recursive RANdom SAmple Consensus
44 SDK Software Development Kit
45 SHOT Signature of Histograms of OrienTations
46 SIFT Scale-Invariant Feature Transform
47 SQ SuperQuadric
48 SURF Speeded Up Robust Features
49 SVM Support Vector Machine
50 TN True Negative
51 TP True Positive
52 TTS Text To Speech
53 UPC Universal Product Code
54 URL Uniform Resource Locator
55 USAC A Universal Framework for Random SAmple Consensus
56 VFH Viewpoint Feature Histogram
57 VIP Visually Impaired Person
57 VIPs Visually Impaired People
vii
LIST OF TABLES
Table 2.1 The number of frames of each scene. . . . . . . . . . . . . . . . . 36
Table 2.2 The average result of detected table plane on our own dataset(%). 41
Table 2.3 The average result of detected table plane on the dataset [117] (%). 43
Table 2.4 The average result of detected table plane of our method with
dierent down sampling factors on our dataset. . . . . . . . . . . . . . 44
Table 3.1 The characteristics of the generated cylinder, sphere, cone dataset
(synthesized dataset) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Table 3.2 The average evaluation results of synthesized datasets. The syn-
thesized datasets were repeated 50 times for statistically representative
results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Table 3.3 Experimental results on the 'second cylinder' dataset. The exper-
iments were repeated 20 times, then errors are averaged. . . . . . . . . 75
Table 3.4 The average evaluation results on the 'second sphere', 'second
cone' datasets. The real datasets were repeated 20 times for statistically
representative results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
Table 3.5 Average results of the evaluation measurements using GCSAC and
MLESAC on three datasets. The tting procedures were repeated 50
times for statistical evaluations. . . . . . . . . . . . . . . . . . . . . . . 83
Table 4.1 The average result detecting spherical objects on two stages. . . . 102
Table 4.2 The average results of detecting the cylindrical objects at the rst
stage in both the rst and second datasets. . . . . . . . . . . . . . . . . 103
Table 4.3 The average results of detecting the cylindrical objects at the
second stage in both the rst and second datasets. . . . . . . . . . . . . 106
Table 4.4 The average processing time of detecting cylindrical objects in
both the rst and second datasets. . . . . . . . . . . . . . . . . . . . . 106
Table 4.5 The average results of 3-D queried objects detection. . . . . . . . 116
viii
LIST OF FIGURES
Figure 1 Illustration of a real scenario: a VIP comes to the Kitchen and
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Figure 2 Illustration of the process of 3-D query-based object in the indoor
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PUBLICATIONS OF DISSERTATION
[1] Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi Lan Le, and Thanh Hai Tran
(2015). Table plane detction using geometrical constraints on depth image, The 8th
Vietnamese Conference on Fundamental and Applied IT Research, FAIR, Hanoi,
VietNam, ISBN: 978-604-913-397-8, pp.647-657.
[2] Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thi-Thanh-Hai Tran,
Michiel Vlaminck, Wilfried Philips and Peter Veelaert. (2015). 3D Object Finding
Using Geometrical Constraints on Depth Images, The 7th International Conference
on Knowledge and Systems Engineering, HCM city, Vietnam, ISBN 978-1-4673-
8013-3, pp.389-395.
[3] Van-Hung Le, Thi-Lan Le, Hai Vu, Thuy Thi Nguyen, Thanh-Hai Tran,
TranChung Dao and Hong-Quan Nguyen (2016), Geometry-based 3-D Object Fitting
and Localization in Grasping Aid for Visually Impaired People, The 6th International
Conference on Communications and Electronics (IEEE-ICCE), HaLong, Vietnam,
ISBN: 978-1-5090-1802-4, pp.597-603.
[4] Van-Hung Le, Michiel Vlaminck, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le,
ThanhHai Tran, Quang-Hiep Luong, Peter Veelaert and Wilfried Philips (2016),
Real-time table plane detection using accelerometer and organized point cloud data
from Kinect sensor, Journal of Computer Science and Cybernetics, Vol. 32, N.3,
ISSN: 1813-9663, pp. 243-258.
[5] Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thanh-Hai Tran (2017),
Fitting Spherical Objects in 3-D Point Cloud Using the Geometrical constraints.
Journal of Science and Technology, Section in Information Technology and Commu-
nications, Number 11, 12/2017, ISSN: 1859-0209, pp 5-17.
[6] Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thanh-Hai Tran (2018),
Acquiring qualied samples for RANSAC using geometrical constraints, Pattern
Recognition Letters, Vol. 102, ISSN: 0167-8655, pp. 58-66, (ISI).
[7] Van-Hung Le, Hai Vu, Thuy Thi Nguyen (2018), A Comparative Study on Detec-
tion and Estimation of a 3-D Object Model in a Complex Scene, 10th International
Conference on Knowledge and Systems Engineering (KSE 2018), pp. 203-208.
[8] Van-Hung Le, Hai Vu, Thuy Thi Nguyen, Thi-Lan Le, Thanh-Hai Tran (2018),
GCSAC: geometrical constraint sample consensus for primitive shapes estimation in
3D point cloud, International Journal Computational Vision and Robotics, Accepted
(SCOPUS).
[9] Van-Hung Le, Hai Vu, Thuy Thi Nguyen (2018), A Frame-work assisting the
Visually Impaired People: Common Object Detection and Pose Estimation in Sur-
rounding Environment, 5th Nafosted Conference on (NICS 2018), pp. 218-223.
[10] Hai Vu, Van-Hung Le, Thuy Thi Nguyen, Thi-Lan Le, Thanh-Hai Tran (2019),
Fitting Cylindrical Objects in 3-D Point Cloud Using the Context and Geometri-
cal constraints, Journal of Information Science and Engineering, ISSN: 1016-2364,
Vol.35, N1, (ISI).
140