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JCR Impact Factor: 0.800
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

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  4/2014 - 11

Visual Peoplemeter: A Vision-based Television Audience Measurement System

SKELIN, A. K. See more information about SKELIN, A. K. on SCOPUS See more information about SKELIN, A. K. on IEEExplore See more information about SKELIN, A. K. on Web of Science, SUPUK, T. G. See more information about  SUPUK, T. G. on SCOPUS See more information about  SUPUK, T. G. on SCOPUS See more information about SUPUK, T. G. on Web of Science, BONKOVIC, M. See more information about BONKOVIC, M. on SCOPUS See more information about BONKOVIC, M. on SCOPUS See more information about BONKOVIC, M. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (835 KB) | Citation | Downloads: 755 | Views: 3,548

Author keywords
consumer behavior, machine vision, object detection, image motion analysis, video surveillance

References keywords
vision(13), recognition(13), tracking(9), pattern(9), attention(9), cvpr(7), machine(6), detection(6), visual(5), television(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04011
Web of Science Accession Number: 000348772500011
SCOPUS ID: 84921682412

Abstract
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Visual peoplemeter is a vision-based measurement system that objectively evaluates the attentive behavior for TV audience rating, thus offering solution to some of drawbacks of current manual logging peoplemeters. In this paper, some limitations of current audience measurement system are reviewed and a novel vision-based system aiming at passive metering of viewers is prototyped. The system uses camera mounted on a television as a sensing modality and applies advanced computer vision algorithms to detect and track a person, and to recognize attentional states. Feasibility of the system is evaluated on a secondary dataset. The results show that the proposed system can analyze viewer's attentive behavior, therefore enabling passive estimates of relevant audience measurement categories.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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