id: 06070261
dt: a
an: 06070261
au: Giannoukos, Ioannis; Vrachnakis, Vassilis; Anagnostopoulos,
    Christos-Nikolaos; Anagnostopoulos, Ioannis; Loumos, Vassili
ti: Block operator context scanning for commercial tracking.
so: Maglogiannis, Ilias (ed.) et al., Artificial intelligence: Theories and
    applications. 7th Hellenic conference on AI, SETN 2012, Lamia, Greece,
    May 28‒31, 2012. Proceedings. Berlin: Springer (ISBN
    978-3-642-30447-7/pbk). Lecture Notes in Computer Science 7297. Lecture
    Notes in Artificial Intelligence, 369-374 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: block-operator context scanning; commercial tracking; sliding windows;
    video matching
ci: 
li: doi:10.1007/978-3-642-30448-4_47
ab: Summary: 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.
rv: