{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/103134"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/103134","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"An Expected Entropy Reduction (EER) Approach to Target Tracking in Maritime Environment for On-board Camera","abstract":"Maritime surveillance system installed on-board is crucial in protecting commercial vessels worldwide, which suffer potential losses due to the maritime piracy assault. On-board camera provides a way of monitoring suspicious activities at a low cost. However, the maritime environment poses challenges in detecting mobile targets because the water background is highly dynamic. This thesis addresses the problem of detecting and tracking mobile targets in the maritime environment utilizing an on-board camera. An approach based on optical flow is used to detect mobile targets presented in the camera scene. The camera measurement model and target kinematics model are proposed such that targets can be tracked utilizing the Bayesian filtering technique. A sensor planning strategy based on expected entropy reduction (EER) is developed to select optimal sensor field of view (FoV) locations such that the expected entropy reduction is maximized.","abstract_html":"Maritime surveillance system installed on-board is crucial in protecting commercial vessels worldwide, which suffer potential losses due to the maritime piracy assault. On-board camera provides a way of monitoring suspicious activities at a low cost. However, the maritime environment poses challenges in detecting mobile targets because the water background is highly dynamic. This thesis addresses the problem of detecting and tracking mobile targets in the maritime environment utilizing an on-board camera. An approach based on optical flow is used to detect mobile targets presented in the camera scene. The camera measurement model and target kinematics model are proposed such that targets can be tracked utilizing the Bayesian filtering technique. A sensor planning strategy based on expected entropy reduction (EER) is developed to select optimal sensor field of view (FoV) locations such that the expected entropy reduction is maximized.","abstract_has_math":false,"creators":["Gao, Xinyu"],"institution":"Cornell University","degree_name":"M.S., Mechanical Engineering","degree_level":"Master of Science","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Hariharan, Bharath"],"year":2020,"date_issued":"2020-08","date_published":"2020-08","updated_at":"2026-07-24T01:49:08Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/51wz-9d62"],"render_values":[{"text":"https://doi.org/10.7298/51wz-9d62","href":"https://doi.org/10.7298/51wz-9d62","code":true}]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11038","ProQuest Publication ID: 28089028"],"render_values":[{"text":"ProQuest Submission ID: 11038","href":null,"code":true},{"text":"ProQuest Publication ID: 28089028","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1813/103134","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hariharan, Bharath"]},{"key":"dc:creator","label":"Author","values":["Gao, Xinyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-03-12T17:43:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-03-12T17:43:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-08"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master of Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S., Mechanical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Cornell University"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/51wz-9d62"]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11038","ProQuest Publication ID: 28089028"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1813/103134"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["56 pages"]},{"key":"dc:description.abstract","label":"Abstract","values":["Maritime surveillance system installed on-board is crucial in protecting commercial vessels worldwide, which suffer potential losses due to the maritime piracy assault. On-board camera provides a way of monitoring suspicious activities at a low cost. However, the maritime environment poses challenges in detecting mobile targets because the water background is highly dynamic. This thesis addresses the problem of detecting and tracking mobile targets in the maritime environment utilizing an on-board camera. An approach based on optical flow is used to detect mobile targets presented in the camera scene. The camera measurement model and target kinematics model are proposed such that targets can be tracked utilizing the Bayesian filtering technique. A sensor planning strategy based on expected entropy reduction (EER) is developed to select optimal sensor field of view (FoV) locations such that the expected entropy reduction is maximized."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An Expected Entropy Reduction (EER) Approach to Target Tracking in Maritime Environment for On-board Camera"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Hariharan, Bharath"],"dc:creator":["Gao, Xinyu"],"dc:date.accessioned":["2021-03-12T17:43:53Z"],"dc:date.available":["2021-03-12T17:43:53Z"],"dc:date.issued":["2020-08"],"dc:description":["56 pages"],"dc:description.abstract":["Maritime surveillance system installed on-board is crucial in protecting commercial vessels worldwide, which suffer potential losses due to the maritime piracy assault. On-board camera provides a way of monitoring suspicious activities at a low cost. However, the maritime environment poses challenges in detecting mobile targets because the water background is highly dynamic. This thesis addresses the problem of detecting and tracking mobile targets in the maritime environment utilizing an on-board camera. An approach based on optical flow is used to detect mobile targets presented in the camera scene. The camera measurement model and target kinematics model are proposed such that targets can be tracked utilizing the Bayesian filtering technique. A sensor planning strategy based on expected entropy reduction (EER) is developed to select optimal sensor field of view (FoV) locations such that the expected entropy reduction is maximized."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["https://doi.org/10.7298/51wz-9d62"],"dc:identifier.other":["ProQuest Submission ID: 11038","ProQuest Publication ID: 28089028"],"dc:identifier.uri":["https://hdl.handle.net/1813/103134"],"dc:language.iso":["en"],"dc:title":["An Expected Entropy Reduction (EER) Approach to Target Tracking in Maritime Environment for On-board Camera"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Master of Science"],"thesis:degree_name":["M.S., Mechanical Engineering"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:49:08Z"}