Date of Award
Fall 12-20-2024
Level of Access Assigned by Author
Open-Access Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering
Advisor
Vikas Dhiman
Second Committee Member
Prabuddha Chakraborty
Third Committee Member
Taher Ghomian
Additional Committee Members
Sidike Paheding
Vijay Devabhaktuni
Abstract
Cross-view geo-localization has garnered notable attention in the realm of computer vision, driven by the widespread availability of geotagged datasets and advancements in machine learning techniques. This thesis provides a comprehensive survey of cutting-edge methodologies, techniques, and associated challenges integral to this domain, focusing on feature-based and deep learning strategies. Feature-based methods utilize unique features to establish correspondences across different viewpoints, while deep learning methodologies employ convolutional neural networks to embed view-invariant attributes. Building on this survey, the research selects the TransGeo model for its promising approach to addressing the limitations of CNN-based methods. The experiments incorporate segmentation techniques and Bird’s Eye View (BEV) transformations in TransGeo. The thesis concludes by exploring future research avenues and highlighting the potential applications of cross-view geo-localization in an increasingly interconnected global landscape.
Recommended Citation
Durgam, Abhilash, "Bridging the Domain Gap in the Transformer Era for Cross View Geo-Localization" (2024). Electronic Theses and Dissertations. 4100.
https://digitalcommons.library.umaine.edu/etd/4100
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