{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109359"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109359","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic object tracking and classification from a moving platform","abstract":"This thesis presents an object-level SLAM system capable of tracking objects in frame and classifying stationary and moving objects. The system combines two open-source algorithms, Mask R-CNN and ORB-SLAM. Mask R-CNN provides instance-level object detection and segmentation, while ORB-SLAM provides keypoint detection, camera tracking, and local mapping. A typical SLAM system assumes a static environment and treats dynamic objects in the scene as outliers. By using object-level information from Mask R-CNN, we extend the capability to recognize and track dynamic objects. 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The system uses only monocular images as input, resulting in numerous potentially low-cost applications without the need for calibrating multiple sensors or using a stereo rig. This system gives a mobile agent the capability of understanding and potentially interacting with its dynamic environment.","abstract_has_math":false,"creators":["Lai, Andy"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh N"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:36:56Z","date_published":"2021-03-05T21:36:56Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Computer vision","object recognition","object tracking","SLAM"],"languages":["en"],"rights":["Copyright 2020 Andy Lai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109359","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N"]},{"key":"dc:creator","label":"Author","values":["Lai, Andy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:36:56Z","2020-11-16","2020-12"]},{"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":["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","object recognition","object tracking","SLAM"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Andy Lai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109359"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis presents an object-level SLAM system capable of tracking objects in frame and classifying stationary and moving objects. 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A typical SLAM system assumes a static environment and treats dynamic objects in the scene as outliers. By using object-level information from Mask R-CNN, we extend the capability to recognize and track dynamic objects. The system uses only monocular images as input, resulting in numerous potentially low-cost applications without the need for calibrating multiple sensors or using a stereo rig. This system gives a mobile agent the capability of understanding and potentially interacting with its dynamic environment.","Submission original under an indefinite embargo labeled 'Open Access'. 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