{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-2167"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-2167","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"Object Detection and Tracking Using Uncalibrated Cameras","abstract":"This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over frames, tracking the object over frames and analyzing object's motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms.","abstract_html":"This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over frames, tracking the object over frames and analyzing object's motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms.","abstract_has_math":false,"creators":["Amara, Ashwini"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Li, Xiao-Rong; Chen, Huimin","Jilkov, Vesselin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-05-14T07:00:00Z","date_published":"2010-05-14T07:00:00Z","updated_at":"2026-07-24T05:29:08Z","subjects":["Object detection and tracking","uncalibrated camera","camera calibration","perspective projection model","feature detection","Nonlinear least squares","lsqnonlin","estimation of camera parameters","azimuth and elevation angles"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/1184","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Xiao-Rong; Chen, Huimin","Jilkov, Vesselin"]},{"key":"dc:creator","label":"Author","values":["Amara, Ashwini"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Object detection and tracking","uncalibrated camera","camera calibration","perspective projection model","feature detection","Nonlinear least squares","lsqnonlin","estimation of camera parameters","azimuth and elevation angles"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/1184"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over frames, tracking the object over frames and analyzing object's motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms."]},{"key":"dc:title","label":"Title","values":["Object Detection and Tracking Using Uncalibrated Cameras"]}]}],"canonical_facts":{"dc:contributor":["Li, Xiao-Rong; Chen, Huimin","Jilkov, Vesselin"],"dc:creator":["Amara, Ashwini"],"dc:description.abstract":["This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over frames, tracking the object over frames and analyzing object's motion and behavior. The approach contains two stages. First, the problem of camera calibration using a calibration object is studied. This approach retrieves the camera parameters from the known locations of ground data in 3D and their corresponding image coordinates. The next important part of this work is to develop an automated system to estimate the trajectory of the object in 3D from image sequences. This is achieved by combining, adapting and integrating several state-of-the-art algorithms. Synthetic data based on a nearly constant velocity object motion model is used to evaluate the performance of camera calibration and state estimation algorithms."],"dc:identifier":["https://scholarworks.uno.edu/td/1184"],"dc:subject":["Object detection and tracking","uncalibrated camera","camera calibration","perspective projection model","feature detection","Nonlinear least squares","lsqnonlin","estimation of camera parameters","azimuth and elevation angles"],"dc:title":["Object Detection and Tracking Using Uncalibrated Cameras"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:29:08Z"}