Rights statement: © ACM, 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in PerDis '17 Proceedings of the 6th ACM International Symposium on Pervasive Displays http://dx.doi.org/10.1145/3078810.3078823
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Audience monitor
T2 - The 6th ACM International Symposium on Pervasive Displays
AU - Elhart, Ivan
AU - Mikusz, Mateusz Andrzej
AU - Mora, Cristian Gomez
AU - Langheinrich, Marc
AU - Davies, Nigel Andrew Justin
N1 - Conference code: 6TH
PY - 2017/6/7
Y1 - 2017/6/7
N2 - Understanding an audience's behavior is an important aspect of evaluating display installations. In particular, it is important to understand how people move around in the vicinity of displays, including viewer transitions from noticing a display, through approach, to final use of the display. Despite the importance of measuring viewer mobility patterns, there are still relatively few low-cost tools that can be used with research display deployments to capture detailed spatial and temporal behavior of an audience. In this paper, we present an approach to audience monitoring that uses an off-the-shelf depth sensor and open source computer vision algorithms to monitor the space in front of a digital display, tracking presence and movements of both passers-by and display users. We believe that our approach can help display researchers evaluate their public display deployments and improve the level of quantitative data underpinning our field.
AB - Understanding an audience's behavior is an important aspect of evaluating display installations. In particular, it is important to understand how people move around in the vicinity of displays, including viewer transitions from noticing a display, through approach, to final use of the display. Despite the importance of measuring viewer mobility patterns, there are still relatively few low-cost tools that can be used with research display deployments to capture detailed spatial and temporal behavior of an audience. In this paper, we present an approach to audience monitoring that uses an off-the-shelf depth sensor and open source computer vision algorithms to monitor the space in front of a digital display, tracking presence and movements of both passers-by and display users. We believe that our approach can help display researchers evaluate their public display deployments and improve the level of quantitative data underpinning our field.
UR - https://github.com/elhart/audienceMonitor
U2 - 10.1145/3078810.3078823
DO - 10.1145/3078810.3078823
M3 - Conference contribution/Paper
SN - 9781450350457
BT - PerDis '17 Proceedings of the 6th ACM International Symposium on Pervasive Displays
PB - ACM
CY - New York
Y2 - 7 June 2017 through 9 June 2017
ER -