Beyond traditional single object tracking
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Abdelaziz, Omar
issued: 2024-06-20T22:10:46Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Single object tracking is a crucial yet challenging task in computer vision. In this task, a model is given the appearance of an arbitrary object in a sequence of frames. The model is required to track the object in all sequence frames. Traditionally, discriminative correlation filters and Siamese convolutional networks have dominated the field but with limitations when tracking challenging objects such as occluded and long-term tracked objects. However, many techniques backed by recent advancements in machine learning theory, such as masked image modelling, generative adversarial networks, variational autoencoders, and diffusion models, are being increasingly leveraged in single object tracking to provide more customized representations that capture the unique characteristics of the target object. This thesis explores recent advancements in single object tracking by providing three novel contributions. The first contribution is that we comprehensively survey emerging trends in single object tracking, including sequence models, generative models, and self-supervised learning, proposing a novel categorization for single object tracking methods. We further compare existing approaches and identify promising directions for future research. The second contribution focuses on the critical role of bounding box regression within single object tracking. We argue that leveraging the receptive field of convolutional networks is essential for accurate object localization. We introduce two novel bounding box regression networks that achieve superior performance on benchmark datasets compared to existing methods. The third contribution addresses the persistent performance gap between training and test data in single object tracking, particularly for transformer-based trackers. It proposes the Deformable Masking Tracker (DMTrack), which injects deformable convolution within the Vision Transformer (ViT) architecture. DMTrack improves the robustness of attentional features, leading …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88494
note: Beyond Traditional Single ObjectTrackingbyOmar AbdelazizB.Sc., Hons., Ain Shams University, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2024© Omar Abdelaziz 2024The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a thesis/dissertation entitled:Beyond Traditional Single Object Trackingsubmitted by Omar Abdelaziz in partial fulfillment of the requirements ofthe degree of the degree of Master of ScienceMohamed S. Shehata, The University of British Columbia, Okanagan CampusSupervisorMohamed Abdelpakey Mohamed, The University of British Columbia, Okanagan CampusCo-SupervisorPourang Irani, The University of British Columbia, Okanagan CampusSupervisory Committee MemberShan Du, The University of British Columbia, Okanagan CampusSupervisory Committee MemberMegan Smith, The University of British Columbia, Okanagan CampusUniversity ExamineriiAbstractSingle object tracking is a crucial yet challenging task in computer vision.In this task, a model is given the appearance of an arbitrary object in a se-quence of frames. The model is required to track the object in all sequenceframes. Traditionally, discriminative correlation filters and Siamese convolu-tional networks have dominated the field but with limitations when trackingchallenging objects such as occluded and long-term tracked objects. How-ever, many techniques backed by recent advancements in machine learningtheory, such as masked image modelling, generative adversarial networks,variational autoencoders, and diffusion models, are being increasingly lever-aged in single object tracking to provide more customized representationsthat capture the unique characteristics of the target object.This thesis explores recent advancements in single object tracking byproviding three novel contributions. The first cont…
has type: Thesis/Dissertation
date issued: 2024-09
is shown at: 10.14288/1.0444000
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Shehata, Mohamed S.
contributor: Abdelpakey, Mohamed H.
type: Text
identifier URI: http://hdl.handle.net/2429/88494