{"id":13109,"date":"2021-12-06T18:34:57","date_gmt":"2021-12-06T23:34:57","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=13109"},"modified":"2021-12-06T18:34:57","modified_gmt":"2021-12-06T23:34:57","slug":"tr-04-03-feature-tracking-for-cut-detection","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2004\/tr-04-03-feature-tracking-for-cut-detection\/","title":{"rendered":"TR-04-03: Feature Tracking for Cut Detection"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-04-03
\nJune 2004<\/p>\n

Feature Tracking for Cut Detection<\/h2>\n
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Anthony Whitehead, Prosenjit Bose, Robert Laganiere<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Abstract<\/h3>\n

There has been much work concentrated on creating shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. Cut detection methods can all be classified based on the various inter-frame differencing schemes that they employ. In this work we present a scheme based on stable feature tracking for inter frame differencing. Furthermore, we present a method to stabilize the differences and automatically detect a global threshold to achieve a high detection rate. We compare our scheme against other cut detection techniques on a variety of data sources that have been specifically selected because of the difficulties they present for other differencing techniques due to quick motion, many small shots and computer-generated effects. In this study our goal is to improve the accuracy of cut detection, particularly for difficult image sequences.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

TR-04-03.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Carleton University Technical Report TR-04-03 June 2004 Feature Tracking for Cut Detection Anthony Whitehead, Prosenjit Bose, Robert Laganiere Abstract There has been much work concentrated on creating shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. Cut detection methods can all be classified based […]<\/p>\n","protected":false},"author":49,"featured_media":0,"parent":12325,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"\nTR-04-03: Feature Tracking for Cut Detection - School of Computer Science<\/title>\n<meta name=\"description\" content=\"Carleton University Technical Report TR-04-03 June 2004 Feature Tracking for Cut Detection Anthony Whitehead, Prosenjit Bose, Robert Laganiere Abstract\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2004\/tr-04-03-feature-tracking-for-cut-detection\/\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2004\/tr-04-03-feature-tracking-for-cut-detection\/\",\"url\":\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-2004\/tr-04-03-feature-tracking-for-cut-detection\/\",\"name\":\"TR-04-03: Feature Tracking for Cut Detection - 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