Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby

Research output: Contribution to conferencePaperResearchpeer-review

Standard

Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby. / Wang, Miaosen; Boring, Sebastian; Greenberg, Saul.

2012.

Research output: Contribution to conferencePaperResearchpeer-review

Harvard

Wang, M, Boring, S & Greenberg, S 2012, 'Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby'.

APA

Wang, M., Boring, S., & Greenberg, S. (2012). Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby.

Vancouver

Wang M, Boring S, Greenberg S. Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby. 2012.

Author

Wang, Miaosen ; Boring, Sebastian ; Greenberg, Saul. / Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby. 6 p.

Bibtex

@conference{59c8e995242e4473bbd603bc72ba8204,
title = "Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby",
abstract = "Effective street peddlers monitor passersby, where they tune their message to capture and keep the passerby’s attention over the entire duration of the sales pitch. Similarly, advertising displays in today’s public environments can be more effective if they were able to tune their content in response to how passersby were at- tending them vs. just showing fixed content in a loop. Previously, others have prototyped displays that monitor and react to the presence or absence of a person within a few proxemic (spatial) zones surrounding the screen, where these zones are used as an estimate of attention. However, the coarseness and discrete nature of these zones mean that they cannot respond to subtle changes in the user’s attention towards the display. In this paper, we contribute an extension to existing proxemic models. Our Peddler Framework captures (1) fine-grained continuous proxemic measures by (2) monitoring the passerby’s distance and orientation with respect to the display at all times. We use this information to infer (3) the passerby’s interest or digression of attention at any given time, and (4) their attentional state with respect to their short-term interaction history over time. Depending on this attentional state, we tune content to lead the passerby into a more attentive stage, ultimately resulting in a purchase. We also contribute a prototype of a public advertising display – called Proxemic Peddler – that demonstrates these extensions as applied to content from the Amazon.com website.",
author = "Miaosen Wang and Sebastian Boring and Saul Greenberg",
year = "2012",
month = "6",
day = "4",
language = "English",

}

RIS

TY - CONF

T1 - Proxemic Peddler: A Public Advertising Display that Captures and Preserves the Attention of a Passerby

AU - Wang, Miaosen

AU - Boring, Sebastian

AU - Greenberg, Saul

PY - 2012/6/4

Y1 - 2012/6/4

N2 - Effective street peddlers monitor passersby, where they tune their message to capture and keep the passerby’s attention over the entire duration of the sales pitch. Similarly, advertising displays in today’s public environments can be more effective if they were able to tune their content in response to how passersby were at- tending them vs. just showing fixed content in a loop. Previously, others have prototyped displays that monitor and react to the presence or absence of a person within a few proxemic (spatial) zones surrounding the screen, where these zones are used as an estimate of attention. However, the coarseness and discrete nature of these zones mean that they cannot respond to subtle changes in the user’s attention towards the display. In this paper, we contribute an extension to existing proxemic models. Our Peddler Framework captures (1) fine-grained continuous proxemic measures by (2) monitoring the passerby’s distance and orientation with respect to the display at all times. We use this information to infer (3) the passerby’s interest or digression of attention at any given time, and (4) their attentional state with respect to their short-term interaction history over time. Depending on this attentional state, we tune content to lead the passerby into a more attentive stage, ultimately resulting in a purchase. We also contribute a prototype of a public advertising display – called Proxemic Peddler – that demonstrates these extensions as applied to content from the Amazon.com website.

AB - Effective street peddlers monitor passersby, where they tune their message to capture and keep the passerby’s attention over the entire duration of the sales pitch. Similarly, advertising displays in today’s public environments can be more effective if they were able to tune their content in response to how passersby were at- tending them vs. just showing fixed content in a loop. Previously, others have prototyped displays that monitor and react to the presence or absence of a person within a few proxemic (spatial) zones surrounding the screen, where these zones are used as an estimate of attention. However, the coarseness and discrete nature of these zones mean that they cannot respond to subtle changes in the user’s attention towards the display. In this paper, we contribute an extension to existing proxemic models. Our Peddler Framework captures (1) fine-grained continuous proxemic measures by (2) monitoring the passerby’s distance and orientation with respect to the display at all times. We use this information to infer (3) the passerby’s interest or digression of attention at any given time, and (4) their attentional state with respect to their short-term interaction history over time. Depending on this attentional state, we tune content to lead the passerby into a more attentive stage, ultimately resulting in a purchase. We also contribute a prototype of a public advertising display – called Proxemic Peddler – that demonstrates these extensions as applied to content from the Amazon.com website.

M3 - Paper

ER -

ID: 44308346