Block operator context scanning for commercial tracking

Giannoukos, I; Vrachnakis, V; Anagnostopoulos, C-N; Anagnostopoulos, I; Loumos, V

dc.contributor.author	Giannoukos, I	en
dc.contributor.author	Vrachnakis, V	en
dc.contributor.author	Anagnostopoulos, C-N	en
dc.contributor.author	Anagnostopoulos, I	en
dc.contributor.author	Loumos, V	en
dc.date.accessioned	2014-03-01T02:53:33Z
dc.date.available	2014-03-01T02:53:33Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36418
dc.subject	Block-Operator Context Scanning	en
dc.subject	Commercial Tracking	en
dc.subject	Sliding Windows	en
dc.subject	Video Matching	en
dc.subject.other	Coarse-to-fine strategy	en
dc.subject.other	Image sequence	en
dc.subject.other	Market analysis	en
dc.subject.other	Processing method	en
dc.subject.other	Regions of interest	en
dc.subject.other	Sliding Window	en
dc.subject.other	Speed increase	en
dc.subject.other	Television channel	en
dc.subject.other	Time sliding windows	en
dc.subject.other	Tracker system	en
dc.subject.other	Video matching	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Scanning	en
dc.title	Block operator context scanning for commercial tracking	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-30448-4_47	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-30448-4_47	en
heal.publicationDate	2012	en
heal.abstract	The industry that designs and promotes advertising products in television channels is constantly growing. For effective market analysis and contract validation, various commercial tracker systems are employed. However, these systems mostly rely on heuristics and, since commercial broadcasting varies significantly, are often inaccurate. This paper proposes a commercial tracker system based on the Block Operator Context Scanning (Block - OCS) algorithm, which is both accurate and fast. The proposed method, similar to coarse-to-fine strategies, skips a large portion of the image sequences by focusing only on Regions of Interest. In this paper, a video matching algorithm is also proposed, which compares image sequences using time sliding windows of frames. Experimental results showed 100% accuracy and 50% speed increase compared to traditional block-based processing methods. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-30448-4_47	en
dc.identifier.volume	7297 LNCS	en
dc.identifier.spage	369	en
dc.identifier.epage	374	en