Scale Adaptive Object Tracking With Diverse Ensemble / (Record no. 8810)

MARC details
000 -LEADER
fixed length control field 07773nam a22002537a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210112b2015 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 Sara Maher El-Kerdawy
245 1# - TITLE STATEMENT
Title Scale Adaptive Object Tracking With Diverse Ensemble /
Statement of responsibility, etc. Sara Maher El-Kerdawy
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2015
300 ## - PHYSICAL DESCRIPTION
Extent 100 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, 2015 .
504 ## - Bibliography
Bibliography "Includes bibliographical references"
505 0# - Contents
Formatted contents note Contents:<br/>Chapter 1: Introduction ................................................................................................. 11<br/>1.1. Overview and Motivation .................................................................................... 11<br/>1.2. Contributions........................................................................................................ 14<br/>1.2.1. Multiple Classifier System (Diverse Ensemble) ....................................... 15<br/>1.2.2. Scale Estimation........................................................................................ 15<br/>1.2.3. Multiple Vehicle Detection and Tracking for Traffic Monitoring ............ 16<br/>1.3. Publications .......................................................................................................... 17<br/>1.4. Organization of Thesis ......................................................................................... 17<br/>Chapter 2: State of the Art ............................................................................................ 19<br/>2.1. Overview of Visual Object Tracking Process ...................................................... 20<br/>2.1.1. Initialization .............................................................................................. 20<br/>2.1.2. Appearance Modeling ............................................................................... 22<br/>2.2. Conclusion ........................................................................................................... 31<br/>Chapter 3: Background ................................................................................................. 33<br/>3.1. Dimensionality Reduction ................................................................................... 33<br/>3.1.1. Principal Component Analysais ................................................................ 35<br/>3.1.2. Random Projections .................................................................................. 38<br/>3.2. Multiple Classifier Systems ................................................................................. 41<br/>3.3. Conclusion ........................................................................................................... 44<br/>Chapter 4: An Integrated Framework for Visual Object Tracking ............................... 45<br/>4.1. Introduction .......................................................................................................... 45<br/>vi<br/>4.2. Compressed Features ........................................................................................... 48<br/>4.2.1. Filter Banks ............................................................................................... 48<br/>4.2.2. Measurement Matrix Construction ........................................................... 48<br/>4.3. Model Construction ............................................................................................. 49<br/>4.3.1. Base Classifiers ......................................................................................... 50<br/>4.3.2. Two-Step Detection .................................................................................. 53<br/>4.3.3. Diverse Ensembles .................................................................................... 54<br/>4.4. Conclusions .......................................................................................................... 58<br/>Chapter 5: Results & Discussion .................................................................................. 61<br/>5.1. Datasets ................................................................................................................ 61<br/>5.2. Evaluation metric ................................................................................................. 62<br/>5.3. Quantitative Analysis ........................................................................................... 62<br/>5.3.1. Experimental setup.................................................................................... 62<br/>5.3.2. Ensemble Evaluation ................................................................................ 63<br/>5.3.3. Scale Adaptability Evaluation ................................................................... 67<br/>5.4. Qualitative Analysis ............................................................................................. 67<br/>5.5. Summary .............................................................................................................. 69<br/>Chapter 6: Case Study: Integrated Framework for Traffic Monitoring ........................ 72<br/>6.1. Framework architecture ....................................................................................... 72<br/>6.2. Vehicle Detection ................................................................................................. 74<br/>6.3. Vehicle Tracking .................................................................................................. 77<br/>6.3.1. Tracklet Management ............................................................................... 78<br/>6.4. Data Association .................................................................................................. 79<br/>6.5. System Evaluation ............................................................................................... 81<br/>6.6. Conclusion ........................................................................................................... 82<br/>Chapter 7: Conclusions and future work ...................................................................... 87<br/>7.1. Summary of contributions.................................................................................... 87<br/>7.2. Future Work ......................................................................................................... 88<br/>References .........................................................................................................................
520 3# - Abstract
Abstract Abstract:<br/>unrestricted environment. Even thought VOT problem is well explored in the<br/>computer vision literature, VOT remains challenging and there are lots of<br/>compromises yet to take such as robustness and time performance, adaptability and<br/>drifting. Object tracking is a crucial problem for many applications such as traffic<br/>monitoring in which we will illustrate our tracking algorithm on. Our objective of this<br/>thesis is to develop a scale and appearance adaptive object tracking algorithm using<br/>statistical machine learning techniques that also performs in real-time applications.<br/>We begin by addressing the object tracking problem by exploring two complementary<br/>components. First we explore learning appearance model using a diverse ensemble of<br/>random projections and benefiting from their sparisty yet robustness. Second we<br/>address object scaling problem to alleviate the problems introduced when the object’s<br/>size changes that differ ranging from avoiding false positive learning to updating the<br/>model with the new object appearance.
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
Holdings
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/ SE.S 2015 01/12/2021 01/12/2021 Thesis