MARC details
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04722nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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201210b2022 a|||f bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Khaled Adel Ezzat |
| 245 1# - TITLE STATEMENT |
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| Title |
EVOLUTION OF OBJECT DETECTION, TRACKING, AND MOTION ESTIMATION ALONG WITH DEEP NEURAL NETWORKS/ |
| Statement of responsibility, etc. |
Khaled Adel Ezzat |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
61 p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: <br/>Khaled Foad<br/>Mustafa Elattar<br/> |
| General note |
<br/>Publication:<br/>1-On the Application of Hierarchical Adaptive Structured Mesh" HASM®" Codec for Ultra Large Video Format<br/>https://dl.acm.org/doi/abs/10.1145/3436829.3436870<br/><br/>2-nnDPI: A Novel Deep Packet Inspection Technique Using Word Embedding, Convolutional and Recurrent Neural Networks<br/>https://ieeexplore.ieee.org/abstract/document/9257912<br/><br/>3-On Optimizing the Visual Quality of HASM-Based Streaming—The Study the Sensitivity of Motion Estimation Techniques for Mesh-Based Codecs in Ultra High Definition Large Format Real-Time Video Coding<br/>https://link.springer.com/chapter/10.1007/978-981-33-6129-4_15<br/><br/>4-MinkowRadon: Multi-Object Tracking Using Radon Transformation and Minkowski Distance<br/>https://ieeexplore.ieee.org/abstract/document/9581542 |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2022 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Chapters:<br/>1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2<br/>1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br/>1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>2. MinkowRadon Multi-Object Tracking Technique . . . . . . . . . . . . . . 12<br/>2.1 Dataset - MOTChallenge (MOT20 & MOT17) . . . . . . . . . . . 13<br/>2.2 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br/>2.3 Numerical Results and Discussion . . . . . . . . . . . . . . . . . . . 20<br/>2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23<br/>3. The Adaptive MinkowYolo Multi-Object Detector-Tracker . . . . . . . . 24<br/>3.1 Dataset - MOT + Visual Tracker Benchmark (VTB) . . . . . . . . 26<br/>3.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 27<br/>3.3 Numerical Results and Discussion . . . . . . . . . . . . . . . . . . . 37<br/>3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42<br/>4. Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 43<br/>Bibliography . . . |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>Object Detection, Tracking and Motion Estimation have been a major concern<br/>since the 1970s, from Self Driving Cars, Surveillance Cameras, Industrial robotics,<br/>Traffic monitoring, Medical diagnosis systems, to Activity recognition, are expecting<br/>a huge increase in demand for automated detection-tracking systems. Modern hardware<br/>specifications and evolving deep learning applications with advancement of Computer<br/>Vision and Digital Video Processing are resulting in a massive progress towards<br/>fully automated systems, with all advance models and systems like R-CNN, YOLO,<br/>SSD, and RetinaNet, there will always be a trade-off between precision (mAP) and<br/>speed (FPS) which puts a new limits to computer vision advancement. Technological<br/>merging has the potential to drive the intuition to achieve such advancements, and<br/>overcome some of the existing limitations. Introducement of a combination between<br/>Deep Neural Networks and Digital signal processing to enable once again progress<br/>to be done in improving Object Detection, Tracking and Motion Estimation in a<br/>real-time videos. Utilizing both of the fields, this thesis purposes a complete detection/<br/>tracking framework utilizing YOLO v4 as a state-of-art object detector to<br/>detect the objects in the video sequences. In addition to a novel MinkowRadon tracking<br/>algorithm which utilizes the Radon Transformation and Minkowski Distance to<br/>translates the rest of video frames sequence to the signal’s domain, in an attempt<br/>iv<br/>to tackle extreme object tracking problems found in video sequences like eg. trembling<br/>moving cameras, deformation, motion blur, fast motion, and in-plane rotation.<br/>Tracking through signals have proven with a higher accuracy compared to the stateof-<br/>art tracking techniques that a combination between classical techniques and deep<br/>learning models is sufficient to solve most modern problems. |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English . |
| 650 #4 - Subject |
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| Subject |
Informatics-IFM |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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266 |
| 655 #7 - Index Term-Genre/Form |
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187 |
| 690 ## - Subject |
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266 |