{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125616"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125616","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Models and data sources for economical computer vision in camera networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Snyder, Corey Ethan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh N","Boppart, Stephen","Liang, Zhi-Pei","Schwing, Alexander","Shomorony, Ilan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-11","date_published":"2024-07-11","updated_at":"2026-07-22T22:25:02Z","subjects":["Computer Vision","Machine Learning","Signal Processing","Camera Networks","Pattern Recognition","Cost-constrained Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Corey Snyder"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125616","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N","Boppart, Stephen","Liang, Zhi-Pei","Schwing, Alexander","Shomorony, Ilan"]},{"key":"dc:creator","label":"Author","values":["Snyder, Corey Ethan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-11","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Vision","Machine Learning","Signal Processing","Camera Networks","Pattern Recognition","Cost-constrained Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Corey Snyder"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125616"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Corey Snyder, accepted the attached license on 2024-07-11 at 10:27.","The student, Corey Snyder, submitted this Dissertation for approval on 2024-07-11 at 10:35.","This Dissertation was approved for publication on 2024-07-11 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21067 on 2025-02-04 at 21:04:59","The past decade of machine learning and computer vision research has led to landmark achievements in numerous tasks from image classification to image generation to panoptic segmentation. These advancements are predicated on the ``Big Data'' paradigm where exceptionally large deep learning models, annotated datasets, and expensive computational infrastructure enables such solutions. In this dissertation, we look to settings where model size, amount of data, and computational cost are constrained. We refer to such settings as ``economical computer vision''. In particular, we focus on camera networks as our target environment and motivate the need for economical solutions for this setting. We present two datasets, STREETS and its extension STREETS BFS, as highly practical demonstrations of how camera networks may be used for intelligent transportation systems across a large suburban county. We shape our development of both datasets through conversations with traffic engineers who operate this network and present benchmark tasks and experiments directed at economical solutions for these engineers. We also propose computer vision models aimed at efficient and effective solutions for the task of background foreground separation (BFS). We introduce our RUSTIC model, the first known algorithm to combine radar and camera modalities, as a multi-model and unsupervised solution for BFS. RUSTIC employs the technique of algorithm unrolling to yield a fast fusion of radar data within the Robust PCA framework. The impressive multi-scene generalization of RUSTIC then motivates our proposed GrayNet model which combines white-box algorithm unrolling with popular black-box deep learning models. We see through rigorous empirical evaluation and ablation studies that GrayNet is a highly label-efficient, interpretable, and fast solution for BFS that may enable economical computer vision in camera networks as well as motivate a variety of future works that extend the work of this dissertation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Models and data sources for economical computer vision in camera networks"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh N","Boppart, Stephen","Liang, Zhi-Pei","Schwing, Alexander","Shomorony, Ilan"],"dc:creator":["Snyder, Corey Ethan"],"dc:date":["2024-07-11","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Corey Snyder, accepted the attached license on 2024-07-11 at 10:27.","The student, Corey Snyder, submitted this Dissertation for approval on 2024-07-11 at 10:35.","This Dissertation was approved for publication on 2024-07-11 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21067 on 2025-02-04 at 21:04:59","The past decade of machine learning and computer vision research has led to landmark achievements in numerous tasks from image classification to image generation to panoptic segmentation. These advancements are predicated on the ``Big Data'' paradigm where exceptionally large deep learning models, annotated datasets, and expensive computational infrastructure enables such solutions. In this dissertation, we look to settings where model size, amount of data, and computational cost are constrained. We refer to such settings as ``economical computer vision''. In particular, we focus on camera networks as our target environment and motivate the need for economical solutions for this setting. We present two datasets, STREETS and its extension STREETS BFS, as highly practical demonstrations of how camera networks may be used for intelligent transportation systems across a large suburban county. We shape our development of both datasets through conversations with traffic engineers who operate this network and present benchmark tasks and experiments directed at economical solutions for these engineers. We also propose computer vision models aimed at efficient and effective solutions for the task of background foreground separation (BFS). We introduce our RUSTIC model, the first known algorithm to combine radar and camera modalities, as a multi-model and unsupervised solution for BFS. RUSTIC employs the technique of algorithm unrolling to yield a fast fusion of radar data within the Robust PCA framework. The impressive multi-scene generalization of RUSTIC then motivates our proposed GrayNet model which combines white-box algorithm unrolling with popular black-box deep learning models. We see through rigorous empirical evaluation and ablation studies that GrayNet is a highly label-efficient, interpretable, and fast solution for BFS that may enable economical computer vision in camera networks as well as motivate a variety of future works that extend the work of this dissertation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125616"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Corey Snyder"],"dc:subject":["Computer Vision","Machine Learning","Signal Processing","Camera Networks","Pattern Recognition","Cost-constrained Machine Learning"],"dc:title":["Models and data sources for economical computer vision in camera networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}