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: Dr Vu Hai 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: Dr Vu Hai 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 confirm 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 qualification 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 attributed 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 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 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 financial support I also gratefully acknowledge the financial 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 sacrifices 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 LITERATURE REVIEW 1.1 Aided-systems for supporting visually impaired people 1.1.1 Aided-systems for navigation services 1.1.2 Aided-systems for obstacle detection 1.1.3 Aided-systems for locating the interested objects in scenes 1.1.4 Discussions 1.2 3-D object detection, recognition from a point cloud data 1.2.1 Appearance-based methods 1.2.1.1 Discussion 1.2.2 Geometry-based methods 1.2.3 Datasets for 3-D object recognition 1.2.4 Discussions 1.3 Fitting primitive shapes 1.3.1 Linear fitting algorithms 1.3.2 Robust estimation algorithms 1.3.3 RANdom SAmple Consensus (RANSAC) and its variations 1.3.4 Discussions 8 11 12 13 13 16 16 17 17 18 18 19 20 23 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 2.2.3 2.3 Related Work The proposed method 2.2.3.1 The proposed framework 2.2.3.2 Plane segmentation 2.2.3.3 Table plane detection and extraction 2.2.4 Experimental results 2.2.4.1 Experimental setup and dataset collection 2.2.4.2 Table plane detection evaluation method 2.2.4.3 Results Separating the interested objects on the table plane 2.3.1 Coordinate system transformation 2.3.2 Separating table plane and the interested objects 2.3.3 Discussions PRIMITIVE SHAPES ESTIMATION BY A NEW ROBUST ESTIMATOR USING GEOMETRICAL CONSTRAINTS 3.1 Fitting primitive shapes by GCSAC 3.1.1 Introduction 3.1.2 Related work 3.1.3 The proposed a new robust estimator 3.1.3.1 Overview of the proposed robust estimator (GCSAC) 3.1.3.2 Geometrical analyses and constraints for qualifying good samples 3.1.4 Experimental results of robust estimator 3.1.4.1 Evaluation datasets of robust estimator 3.1.4.2 Evaluation measurements of robust estimator 3.1.4.3 Evaluation results of a new robust estimator 3.1.5 Discussions 3.2 Fitting objects using the context and geometrical constraints 3.2.1 The proposed method of finding objects using the context and geometrical constraints 3.2.1.1 Model verification using contextual constraints 3.2.2 Experimental results of finding objects using the context and geometrical constraints 3.2.2.1 Descriptions of the datasets for evaluation 3.2.2.2 Evaluation measurements 3.2.2.3 Results of finding objects using the context and geometrical constraints 3.2.3 Discussions iv 29 30 30 32 34 36 36 37 40 46 46 48 48 51 52 52 53 55 55 58 64 64 67 68 74 76 77 77 78 78 81 82 85 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 different 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 models (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 finding 3-D objects 115 4.2.4 Evaluation of usability and discussion 118 CONCLUSION AND FUTURE WORKS 121 5.1 Conclusion 121 5.2 Future works 123 Bibliography 125 PUBLICATIONS 139 v ABBREVIATIONS No Abbreviation Meaning API Application Programming Interface CNN Convolution Neural Network CPU Central Processing Unit CVFH Clustered Viewpoint Feature Histogram FN False Negative FP False Positive FPFH Fast Point Feature Histogram fps f rame per second GCSAC Geometrical Constraint SAmple Consensus 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 Modified 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 IDentification 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 different 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 synthesized datasets were repeated 50 times for statistically representative results 75 Table 3.3 Experimental results on the ’second cylinder’ dataset The experiments 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 fitting 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 first stage in both the first and second datasets 103 Table 4.3 The average results of detecting the cylindrical objects at the second stage in both the first and second datasets 106 Table 4.4 The average processing time of detecting cylindrical objects in both the first and second 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Comparative Study on Detection 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 Surrounding 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 Geometrical constraints, Journal of Information Science and Engineering, ISSN: 1016-2364, Vol.35, N1, (ISI) 140 ... used for 3- D object detection In Chapter 4, we revisit 3- D object detection in which relevant ones are presented more details 12 1.2 3- D object detection, recognition from a point cloud data 3- D. .. using the proposed methods for detecting 3- D primitive shape objects in a lab-based environment The system combined the table plane detection technique and the proposed method of 3- D objects detection. .. tasks including: (1) separating the queried objects from a table plane; (2) detecting candidates of the interested objects using appearance features; and (3) estimating a model of the queried-object