{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2016"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2016","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Deep learning-based framework for traffic estimation for the MLK Smart Corridor in downtown Chattanooga, TN","abstract":"In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes with a reported mean average precision of 0.69. For re-identification, we refined the DeepSort tracker, a tracking-by-detection model. We incorporated a Siamese network in place of its default feature extractor. Both models were trained on the UA-DETRAC dataset, tested on KITTI, revealing a 71\\% reduction in the IDSW rate with our revision. Movement direction classification, an offline system component, utilized a similarity-based trajectory method with specific spatial constraints. Finally we combined image perspective transformation with objects scaling to estimate speed with an error of 0.516 mph. Our comprehensive framework offers potential in applications like travel time estimation and benchmarking speed data.","abstract_html":"In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes with a reported mean average precision of 0.69. For re-identification, we refined the DeepSort tracker, a tracking-by-detection model. We incorporated a Siamese network in place of its default feature extractor. Both models were trained on the UA-DETRAC dataset, tested on KITTI, revealing a 71\\% reduction in the IDSW rate with our revision. Movement direction classification, an offline system component, utilized a similarity-based trajectory method with specific spatial constraints. Finally we combined image perspective transformation with objects scaling to estimate speed with an error of 0.516 mph. Our comprehensive framework offers potential in applications like travel time estimation and benchmarking speed data.","abstract_has_math":false,"creators":["Hassan, Yasir"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sartipi, Mina","Liang, Yu; Wu, Dalei","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:13Z","subjects":["Deep learning (Machine learning)","Image processing--Digital techniques"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/841","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sartipi, Mina","Liang, Yu; Wu, Dalei","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Hassan, Yasir"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning (Machine learning)","Image processing--Digital techniques"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/841"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes with a reported mean average precision of 0.69. For re-identification, we refined the DeepSort tracker, a tracking-by-detection model. We incorporated a Siamese network in place of its default feature extractor. Both models were trained on the UA-DETRAC dataset, tested on KITTI, revealing a 71\\% reduction in the IDSW rate with our revision. Movement direction classification, an offline system component, utilized a similarity-based trajectory method with specific spatial constraints. Finally we combined image perspective transformation with objects scaling to estimate speed with an error of 0.516 mph. Our comprehensive framework offers potential in applications like travel time estimation and benchmarking speed data."]},{"key":"dc:title","label":"Title","values":["Deep learning-based framework for traffic estimation for the MLK Smart Corridor in downtown Chattanooga, TN"]}]}],"canonical_facts":{"dc:contributor":["Sartipi, Mina","Liang, Yu; Wu, Dalei","College of Engineering and Computer Science"],"dc:creator":["Hassan, Yasir"],"dc:date":["2023-12-01T08:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes with a reported mean average precision of 0.69. For re-identification, we refined the DeepSort tracker, a tracking-by-detection model. We incorporated a Siamese network in place of its default feature extractor. Both models were trained on the UA-DETRAC dataset, tested on KITTI, revealing a 71\\% reduction in the IDSW rate with our revision. Movement direction classification, an offline system component, utilized a similarity-based trajectory method with specific spatial constraints. 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