{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/6664"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/6664","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Anomalous Motion Detection of Vehicles on Highway using Deep Learning","abstract":"Research in visual anomaly detection draws much interest due to applications in surveillance. Common data sets for evaluation are constructed using a stationary camera overlooking an area of interest. Despite the challenges of learning from a single class of data in an unsupervised learning paradigm, previous research shows promising results in detecting spatial as well as temporal anomalies in crowded environments. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no data set exists to evaluate anomaly detection models in this setting. This thesis presents a novel anomaly detection data set for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. I evaluate state-of-the-art unsupervised deep learning anomaly detection models as well as propose novel variations and discuss the exacerbated challenges of this new data set.","abstract_html":"Research in visual anomaly detection draws much interest due to applications in surveillance. Common data sets for evaluation are constructed using a stationary camera overlooking an area of interest. Despite the challenges of learning from a single class of data in an unsupervised learning paradigm, previous research shows promising results in detecting spatial as well as temporal anomalies in crowded environments. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no data set exists to evaluate anomaly detection models in this setting. This thesis presents a novel anomaly detection data set for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. I evaluate state-of-the-art unsupervised deep learning anomaly detection models as well as propose novel variations and discuss the exacerbated challenges of this new data set.","abstract_has_math":false,"creators":["Singh, Harpreet"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Alexis, Kostas"],"committee_chairs":[],"committee_members":["Hand, Emily","Panorska, Anna"],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-27T21:47:58Z","subjects":["anomaly detection","deep learning","machine learning","one class classification"],"languages":[],"rights":["Creative Commons Attribution 4.0 United States"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/6664","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alexis, Kostas"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hand, Emily","Panorska, Anna"]},{"key":"dc:creator","label":"Author","values":["Singh, Harpreet"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-01-30T23:11:13Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-30T23:11:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["anomaly detection","deep learning","machine learning","one class classification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 United States"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/6664"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Research in visual anomaly detection draws much interest due to applications in surveillance. 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