{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/918"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/918","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Achieving real-time video summarization on commodity hardware","abstract":"We present a system for automatic video summarization which is able to operate in real-time on commodity hardware. This is achieved by performing segmentation to divide a video into a series of small video clips, which are further reduced or eliminated with the assistance of highly efficient low-level features. A numerical score is then assigned to each segment by our model trained using a set of highperformance hand-crafted features. Finally, segments are selected based on their score to generate a final video summary. On our benchmark dataset, we achieve results competitive to other methods. In cases where our accuracy is lower than competitive methods, we achieve significantly higher performance. We additionally present methods for generating additional summaries almost instantly, and for learning user preferences over time—two processes which are often overlooked in work on video summarization, but essential for real-world use","abstract_html":"We present a system for automatic video summarization which is able to operate in real-time on commodity hardware. This is achieved by performing segmentation to divide a video into a series of small video clips, which are further reduced or eliminated with the assistance of highly efficient low-level features. A numerical score is then assigned to each segment by our model trained using a set of highperformance hand-crafted features. Finally, segments are selected based on their score to generate a final video summary. On our benchmark dataset, we achieve results competitive to other methods. In cases where our accuracy is lower than competitive methods, we achieve significantly higher performance. We additionally present methods for generating additional summaries almost instantly, and for learning user preferences over time—two processes which are often overlooked in work on video summarization, but essential for real-world use","abstract_has_math":false,"creators":["Taylor, Wesley"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Qureshi, Faisal"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-04-01","date_published":"2018-04-01","updated_at":"2026-07-24T05:35:36Z","subjects":["Video summarization","Machine learning","Computer vision"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/918","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Qureshi, Faisal"]},{"key":"dc:creator","label":"Author","values":["Taylor, Wesley"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-06-28T19:49:43Z","2022-03-29T17:25:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-28T19:49:43Z","2022-03-29T17:25:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Video summarization","Machine learning","Computer vision"]}]},{"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.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/918"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We present a system for automatic video summarization which is able to operate in real-time on commodity hardware. This is achieved by performing segmentation to divide a video into a series of small video clips, which are further reduced or eliminated with the assistance of highly efficient low-level features. A numerical score is then assigned to each segment by our model trained using a set of highperformance hand-crafted features. Finally, segments are selected based on their score to generate a final video summary. On our benchmark dataset, we achieve results competitive to other methods. In cases where our accuracy is lower than competitive methods, we achieve significantly higher performance. We additionally present methods for generating additional summaries almost instantly, and for learning user preferences over time—two processes which are often overlooked in work on video summarization, but essential for real-world use"]},{"key":"dc:title","label":"Title","values":["Achieving real-time video summarization on commodity hardware"]}]}],"canonical_facts":{"dc:contributor.advisor":["Qureshi, Faisal"],"dc:creator":["Taylor, Wesley"],"dc:date.accessioned":["2018-06-28T19:49:43Z","2022-03-29T17:25:47Z"],"dc:date.available":["2018-06-28T19:49:43Z","2022-03-29T17:25:47Z"],"dc:date.issued":["2018-04-01"],"dc:description.abstract":["We present a system for automatic video summarization which is able to operate in real-time on commodity hardware. This is achieved by performing segmentation to divide a video into a series of small video clips, which are further reduced or eliminated with the assistance of highly efficient low-level features. A numerical score is then assigned to each segment by our model trained using a set of highperformance hand-crafted features. Finally, segments are selected based on their score to generate a final video summary. On our benchmark dataset, we achieve results competitive to other methods. In cases where our accuracy is lower than competitive methods, we achieve significantly higher performance. We additionally present methods for generating additional summaries almost instantly, and for learning user preferences over time—two processes which are often overlooked in work on video summarization, but essential for real-world use"],"dc:identifier.uri":["https://hdl.handle.net/10155/918"],"dc:language.iso":["en"],"dc:subject":["Video summarization","Machine learning","Computer vision"],"dc:title":["Achieving real-time video summarization on commodity hardware"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:36Z"}