{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/100926"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/100926","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Unsupervised tracking algorithm for precise traffic estimation in panoramic scenes","abstract":"The student, Fangyu Wu, accepted the attached license on 2018-04-04 at 23:48.","abstract_html":"The student, Fangyu Wu, accepted the attached license on 2018-04-04 at 23:48.","abstract_has_math":false,"creators":["Wu, Fangyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Work, Daniel B."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:26:50Z","date_published":"2018-09-04T20:26:50Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Computer Vision","Vehicle Tracking"],"languages":["en"],"rights":["Copyright 2018 Fangyu Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/100926","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Work, Daniel B."]},{"key":"dc:creator","label":"Author","values":["Wu, Fangyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:26:50Z","2018-04-05","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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","Vehicle Tracking"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Fangyu Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/100926"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Fangyu Wu, accepted the attached license on 2018-04-04 at 23:48.","The student, Fangyu Wu, submitted this Thesis for approval on 2018-04-05 at 00:06.","This Thesis was approved for publication on 2018-04-05 at 08:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12122 on 2018-08-31 at 17:10:37","Made available in DSpace on 2018-09-04T20:26:50Z (GMT). No. of bitstreams: 2 WU-THESIS-2018.pdf: 20320817 bytes, checksum: 77fdfca8346f103c79f25ada39900fa5 (MD5) LICENSE.txt: 4206 bytes, checksum: 5f2690b3d067a2a0908cb77866c5ebfc (MD5) Previous issue date: 2018-04-05","The traffic experiment conducted by physicist Sugiyama in 2007 has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform initial spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and stop-and-go waves. This dissertation introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Validation analysis shows that the collected data are highly accurate, with a mean position bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields highly reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. Beyond the experimental methodology, the produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emission. To facilitate future research, the source code and the data are made publicly available online.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Unsupervised tracking algorithm for precise traffic estimation in panoramic scenes"]}]}],"canonical_facts":{"dc:contributor":["Work, Daniel B."],"dc:creator":["Wu, Fangyu"],"dc:date":["2018-09-04T20:26:50Z","2018-04-05","2018-05"],"dc:description":["The student, Fangyu Wu, accepted the attached license on 2018-04-04 at 23:48.","The student, Fangyu Wu, submitted this Thesis for approval on 2018-04-05 at 00:06.","This Thesis was approved for publication on 2018-04-05 at 08:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12122 on 2018-08-31 at 17:10:37","Made available in DSpace on 2018-09-04T20:26:50Z (GMT). No. of bitstreams: 2 WU-THESIS-2018.pdf: 20320817 bytes, checksum: 77fdfca8346f103c79f25ada39900fa5 (MD5) LICENSE.txt: 4206 bytes, checksum: 5f2690b3d067a2a0908cb77866c5ebfc (MD5) Previous issue date: 2018-04-05","The traffic experiment conducted by physicist Sugiyama in 2007 has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform initial spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and stop-and-go waves. This dissertation introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Validation analysis shows that the collected data are highly accurate, with a mean position bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields highly reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. Beyond the experimental methodology, the produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emission. To facilitate future research, the source code and the data are made publicly available online.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/100926"],"dc:language":["en"],"dc:rights":["Copyright 2018 Fangyu Wu"],"dc:subject":["Computer Vision","Vehicle Tracking"],"dc:title":["Unsupervised tracking algorithm for precise traffic estimation in panoramic scenes"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:38Z"}