An Approach of Features Extraction and Heatmaps Generation Based Upon Cnns and 3D Object Models

University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 9-27-2019 An Approach Of Features Extraction And Heatmaps Generation Based Upon Cnns And 3D Object Models Shivani Pachika University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Pachika, Shivani, "An Approach Of Features Extraction And Heatmaps Generation Based Upon Cnns And 3D Object Models" (2019). Electronic Theses and Dissertations. 7830. https://scholar.uwindsor.ca/etd/7830 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). 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AN APPROACH OF FEATURES EXTRACTION AND HEAT MAPS GENERATION BASED UPON CNNS AND 3D OBJECT MODELS By Shivani Pachika A Thesis Submitted to the Faculty of Graduate Studies through the School of Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Science at the University of Windsor Windsor, Ontario, Canada 2019 © 2019 Shivani Pachika AN APPROACH OF FEATURES EXTRACTION AND HEAT MAPS GENERATION BASED UPON CNNS AND 3D OBJECT MODELS by Shivani Pachika APPROVED BY: ______________________________________________ M. Hlynka Department of Mathematics and Statistics ______________________________________________ A. Ngom School of Computer Science ______________________________________________ X. Yuan, Advisor School of Computer Science September 27th, 2019 DECLARATION OF ORIGINALITY I hereby certify that I am the sole author of this thesis and that no part of this thesis has been published or submitted for publication. I certify that, to the best of my knowledge, my thesis does not infringe upon anyone’s copyright nor violate any proprietary rights. Any ideas, techniques, quotations, or any other material from the work of other people included in my thesis, published or otherwise, are fully acknowledged in accordance with the standard referencing practices. Furthermore, to the extent that I have included copyrighted material that surpasses the bounds of fair dealing within the meaning of the Canada Copyright Act, I certify that I have obtained a written permission from the copyright owner(s) to include such material(s) in my thesis and have added copies of such copyright clearances to my appendix. I also declare that this is an exact copy of my thesis, including any final revisions, as approved by my thesis committee and the Graduate Studies office and that this thesis has not been submitted for a higher degree to any other University or Institution. iii ABSTRACT The rapid advancements in artificial intelligence have enabled recent progress of self- driving vehicles. However, the dependence on 3D object models and their annotations collected and owned by individual companies has become a major problem for the development of new algorithms. This thesis proposes an approach of directly using graphics models created from open-source datasets as the virtual representation of real- world objects. This approach uses Machine Learning techniques to extract 3D feature points and to create annotations from graphics models for the recognition of dynamic objects, such as cars, and for the verification of stationary and variable objects, such as buildings and trees. Moreover, it generates heat maps for the elimination of stationary/variable objects in real-time images before working on the recognition of dynamic objects. The proposed approach helps to bridge the gap between the virtual and physical worlds and to facilitate the development of new algorithms for self-driving vehicles. iv DEDICATION To the almighty God, my beloved parents Mr. Devender Reddy and Mrs. Jaya, my loving sister Miss Bhavishya, for their unconditional love, support, sacrifice, and encouragement. v ACKNOWLEDGMENTS I would like to take this opportunity to thank my supervisor Dr. Xiaobu Yuan for his encouragement and support in presenting this thesis work. I have been extremely fortunate to have a supervisor who has given me the freedom to explore things on my own, and with his patient guidance whenever my steps faltered. His kindness and support have helped me overcome many problems, throughout my project. It has been a great pleasure to work under him as one of his research project students. I would like to extend my gratitude to my thesis committee members Dr. Myron Hlynka (Department of Mathematics and Statistics) and Dr. Alioune Ngom (School of Computer Science) whose suggestions and recommendations significantly improved the quality of this work. I would also like to thank them for spending their valuable time to assess the thesis from my proposal to defense. I would also like to thank the secretaries of the Computer science department, Mrs. Christine Weisener and Mrs. Melissa Robinet for all the assistance, motivation and support given to me. My special thanks goes to my friends and relatives for their constant encouragement and love they provided to me at all times. I express my sincere appreciation for my fellow lab mates for the invaluable support they provided during all stages of my thesis work. vi TABLE OF CONTENTS Declaration of Originality…………………………………………………...iii Abstract ………………………………………………….………….………iv Dedication ………………………………………………….………….…….v Acknowledgments…………………………………………….……….……vi List of Tables…………………………………………………...……………x List of Abbreviations/Symbols……………………………………………...xi List of Figures...…………………………………………………………….xii 1 Introduction …………………………………………………….….….….1 1.1 Overview...................................................................................................1 1.2 Sensing Technology comparison...………………………………………2 1.3 Problem Statement ...…………………………………………………….3 1.4 Motivation.………………………………………………………………3 1.5 Thesis Contribution……………………………………………………...4 1.6 Structure of the thesis ……………………………………………….…...4 2 Background Study………………………………………...……………...5 2.1 3D model…………………………………………………….…………...5 2.2 Rendering……………………………………………………...…………5 2.3 Virtual 3D city model………………………...……………………...…...5 2.4 3D GIS…………………………………………………………………...6 2.5 Feature…………………………………………………………………...7 2.6 Feature Extraction……………………………………….………….……7 2.7 Feature Selection………………………………………….……………...7 2.8 2D Feature Extraction Techniques…………………………….…………8 2.9 3D Feature Extraction Techniques……………………………………...13 vii 2.10 Computer vision tasks………………………………………………....16 2.11 Object detection Algorithms………………………………….……….18 2.11.1 Traditional methods.………………………………………….19 2.11.2 Deep learning methods……………………………………….20 2.12 Heat maps ………………………………………………………….….28 2.13 Annotations…………………………………………………………....29 3 Literature Survey.……………………………………………………….30 3.1 Static object feature extraction techniques……………………….…......30 3.2 Dynamic object feature extraction techniques………………………......31 3.3 Static and Dynamic object feature extraction techniques…………........35 3.4 Static and Dynamic object detection techniques……………………......38 3.5 Related Works…………………………………………………………..40 4 Proposed Methodology.............................................................................43 4.1 Proposed Methodology Overview ……………………………………...43 4.1.1 Training information ..................................................................44 4.2 Cars..……………………………………………………………………45 4.3 Buildings.……………………………………………………………….48 4.4 Trees …………………………………………...………………….……55 4.5 Traffic light....……………………………………………….………….58 4.6 Working of the overall system...………………………………………...61 4.7 Working of the individual modules..……………………………………62 5 Implementation and Experiments.………………………………….….66 5.1 Software and Hardware Requirements.....………………………………66 5.2 Cars………………………………………………………………….….67 5.3 Buildings.……………………………………………………………….71 5.4 Trees ……………………………………………………………………74 5.5 Traffic lights ……………………………………………………………76 viii 6 Results and Evaluation...…………………………………………….….78 6.1 Cars..........................................................................................................78 6.2 Buildings and Trees……….……………………………………………79 6.3 Performances Measures...........................................................................81 6.4 Evaluations and Comparisons…………………………………………..82 6.5 Advantages of proposed methodology.………………………………....84 6.5.1 Advantage to dependency modules…………………………….85 6.6 Limitations…………………………………………….…………….….85 7 Conclusion and Future Works………………………………………….86 7.1 Conclusion…………………………………………….…………….….86 7.2 Future Works …………………………………………………………...86 References/Bibliography………………………………………………….87 Appendix …………………………………………………………………..94 Vita Auctoris……………………………………………………………....97 ix LIST OF TABLES Table 1: Sensing Technology Comparison [49]…………………………………………………………………………… 2 Table 2: List of tools used for implementation and experiments .................................................. 66 Table 3: Suwajanakorn, S., et al. 2018 vs Our approach ...............................................................

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