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


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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
 
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Download PDF pdficon (835 KB) | Citation | Downloads: 456 | Views: 2,618

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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Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 22,579 TCR
SCOPUS® Citations for all references: 24,066 TCR

Web of Science® Average Citations per reference: 627 ACR
SCOPUS® Average Citations per reference: 669 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-10-19 07:34 in 183 seconds.




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Stefan cel Mare University of Suceava, Romania


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