Robust Target Detection snf Tracking in Unmanned Aerial Vehicles (UAVs) Imagery (Record no. 8808)
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| 000 -LEADER | |
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| fixed length control field | 10041nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210112b2013 a|||f mb|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mennatullah Mohamed Siam |
| 245 1# - TITLE STATEMENT | |
| Title | Robust Target Detection snf Tracking in Unmanned Aerial Vehicles (UAVs) Imagery |
| Statement of responsibility, etc. | Mennatullah Mohamed Siam |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2013 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| Materials specified | Supervisor: Mohamed A. El-Helw |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2013 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>Chapter1: Introduction ........................................................................................................ 1<br/>1.1 Overview and Motivation ............................................................................... 1<br/>1.2 Problem Definition ......................................................................................... 3<br/>1.3 Contributions .................................................................................................. 3<br/>1.3.1 Real-time Onboard Target Detection and Tracking ................................ 3<br/>1.3.2 Robust Tracking using P-N Learning ..................................................... 4<br/>1.4 Publications .................................................................................................... 5<br/>1.5 Organization of Thesis ................................................................................... 6<br/>Chapter 2: Review of Detection and Tracking in UAV Imagery ....................................... 7<br/>2.1 Unmanned Aerial Vehicles (UAVs) .............................................................. 7<br/>2.1.1 UAVs Types............................................................................................ 7<br/>2.1.2 Existing UAV Models............................................................................. 8<br/>2.1.3 UAVs Applications ............................................................................... 10<br/>2.2 Target Detection and Tracking ..................................................................... 11<br/>2.2.1 Target Detection Taxonomy ................................................................. 11<br/>2.2.2 Target Tracking Taxonomy .................................................................. 13<br/>2.3 Target Detection in UAV Imagery ............................................................... 15<br/>2.3.1 Interest Point Detectors ......................................................................... 15<br/>2.3.2 Motion Detection .................................................................................. 16<br/>2.3.3 Segmentation......................................................................................... 16<br/>2.3.4 Supervised Learning Detection ............................................................. 17<br/>vii<br/>2.3.5 Summary ............................................................................................... 18<br/>2.4 Target Tracking in UAV Imagery ................................................................ 19<br/>2.4.1 Point Tracking ....................................................................................... 19<br/>2.4.2 Kernel Tracking .................................................................................... 20<br/>2.5 State of the Art Trackers .............................................................................. 21<br/>Chapter 3: Integrated Framework for Target Detection and Tracking ............................. 23<br/>3.1 Framework Architecture .............................................................................. 23<br/>3.2 Multiple Target Detection ............................................................................ 24<br/>3.2.1 Feature Extractor ................................................................................... 24<br/>3.2.2 Motion Estimation ................................................................................ 25<br/>3.2.3 Outliers Detection and DBScan Clustering .......................................... 30<br/>3.3. Target Tracking ............................................................................................ 34<br/>3.3.1. Overlap-Rate Based Data Association .................................................. 34<br/>3.3.2. Kalman Prediction ................................................................................ 37<br/>3.3.3. BRIEF Descriptors Search .................................................................... 39<br/>Chapter 4: Enhanced Target Tracking using PN Constraints ........................................... 42<br/>4.1 Enhanced Version Framework Architecture ................................................ 42<br/>4.2 Updated Data Association Algorithm .......................................................... 43<br/>4.3 P-N (Positive-Negative) Learning ................................................................ 44<br/>4.3.1 Semi-supervised Learning Overview .................................................... 44<br/>4.3.2 P-N Learning Approach ........................................................................ 45<br/>4.3.3 P-N Learning in Object Tracking ......................................................... 46<br/>4.4 Novel P-N Experts ....................................................................................... 49<br/>Chapter 5: Results & Discussion ...................................................................................... 51<br/>5.1 Datasets ........................................................................................................ 51<br/>5.1.1. Quad-rotor ............................................................................................. 52<br/>5.1.2. PandaBoard and Middleware ................................................................ 53<br/>5.1.3. Dataset Collection ................................................................................. 54<br/>5.2 Evaluation Test-bed and Metrics .................................................................. 55<br/>5.2.1 Evaluation Test-bed .............................................................................. 55<br/>5.2.2 Evaluation Metrics ................................................................................ 56<br/>viii<br/>5.3 Qualitative Analysis ..................................................................................... 57<br/>5.3.1 Recapture Targets Out/In Camera Field of View: ................................ 61<br/>5.4 Quantitative Evaluation ................................................................................ 63<br/>Chapter 6: Conclusions and Future Work ......................................................................... 69<br/>6.1. Summary of Contributions ........................................................................... 69<br/>6.2. Future Work ................................................................................................. |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>In this dissertation we address the problem of automatic target detection and<br/>tracking in UAV imagery. Even though target detection and tracking are well<br/>explored problems in the field of computer vision, several challenges arise in case of<br/>UAV imagery due to the modus operandi of the latter in terms of operational and<br/>processing constraints. UAVs are airborne platforms that are typically in continuous<br/>and sometimes turbulent rapid motions. The payloads are minimal with limited<br/>processing boards and low-resolution image sensors capturing imagery from high<br/>altitudes. Consequently, on-board vision-based algorithms have to decouple scene<br/>and UAV motions to detect targets of interest, shrewdly exploit low-resolution<br/>imagery to discriminate targets based on appearance, and execute efficiently to meet<br/>the necessary demand of real-time performance. Additional challenges include the<br/>fact that tracking of detected targets should be adaptive and learns the appearance of<br/>the targets to maintain their tracking case of significant illumination changes and<br/>partial occlusions, re-acquire targets as they move out of and subsequently in the<br/>field-of-view, and to discriminate between similar targets moving within close spatial<br/>proximity of each other.<br/>This thesis presents an integrated framework comprising novel set of algorithms<br/>to alleviate the above described problems in order to facilitate on-board robust realtime<br/>target detection and tracking in UAV imagery. The separation of scene and<br/>camera motions from the targets' motion is achieved by using an algorithm based on<br/>image feature processing and projective geometry. Detected targets are tracked with<br/>Kalman filtering while an overlap-rate-based data association mechanism followed by<br/>tracking persistency check are used to discriminate between true moving targets and<br/>false detections. The proposed framework doesn’t involve explicit application of<br/>image transformations to detect potential targets resulting in enhanced computational<br/>time and reduction of registration errors. The tracking performance is further<br/>enhanced by utilizing P-N learning to learn the targets' appearances online. To this<br/>end, novel P-N constraints are introduced based on data association to control the<br/>xiii<br/>positive and negative samples while a cascaded classifier is employed to detect the<br/>targets in case of association failure after learning targets' appearance. Experimental<br/>results obtained with publicly available DARPA aerial datasets demonstrate that the<br/>proposed tracker with automatic detection feedback achieves better recall and average<br/>overlap than existing manually-initialized trackers. Further qualitative analysis proves<br/>the framework is able to solve most of the above challenges including target recapture<br/>and discriminating between targets with similar appearance while alleviating tracker<br/>drift problems. |
| 546 ## - Language Note | |
| Language Note | Text in English, abstracts in English. |
| 650 #4 - Subject | |
| Subject | Informatics-IFM |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 650 #4 - Subject | |
| -- | 266 |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| 690 ## - Subject | |
| -- | 266 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Not For Loan | Main library | Main library | 01/12/2021 | 610/ MS.R 2013 | 01/12/2021 | 01/12/2021 | Thesis |