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Audience Measurement of Digital Signage: Quantitative Study in Real-World Environment Using Computer Vision

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We present a quantitative study of digital signage audience measurement using computer vision. We developed a camera-enhanced digital signage display that acquires audience measurement metrics with computer vision algorithms. Temporal metrics of a person's dwell time, display in-view time and attention time are extracted. The system also determines demographic metrics of the gender and age group. The digital signage display was deployed in a real-world environment of a clothing boutique, where demographic and viewership data of 1294 store customers were recorded, manually verified and analysed. The analysis shows that 35% of customers specifically looked-at the display, having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1–14 years) are the most responsive to the digital signage. Finally, the analysis shows that the average attention time is significantly higher when displaying the dynamic content (0.9 s) when compared with the static content (0.6 s).
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doi:10.1093/iwc/iws023
Audience Measurement of Digital
Signage: Quantitative Study in
Real-World Environment Using
Computer Vision
Robert Ravnik and Franc Solina
Faculty of Computer and Information Science, University of Ljubljana,
Tržaška 25, SI-1000 Ljubljana, Slovenia
Corresponding author: franc.solina@fri.uni-lj.si
We present a quantitative study of digital signage audience measurement using computer vision. We
developed a camera-enhanced digital signage display that acquires audience measurement metrics
with computer vision algorithms. Temporal metrics of a person’s dwell time, display in-view time
and attention time are extracted. The system also determines demographic metrics of the gender
and age group. The digital signage display was deployed in a real-world environment of a clothing
boutique, where demographic and viewership data of 1294 store customers were recorded, manually
verified and analysed. The analysis shows that 35% of customers specifically looked-at the display,
having the average attention time of 0.7 s. Interestingly, the attention time was substantially higher
for men (1.2 s) than for women (0.4 s). Age group comparison reveals that children (1–14 years) are
the most responsive to the digital signage. Finally, the analysis shows that the average attention time
is significantly higher when displaying the dynamic content (0.9 s) when compared with the static
content (0.6 s).
Keywords: digital signage; audience measurement; quantitative study; computer vision
Editorial Board Member: Ruven Brooks
Received 1 May 2012; Revised 3 September 2012; Accepted 12 November 2012
1. INTRODUCTION
Modern applications of digital signage are interfaces to public
or internal information, advertising, brand building and making
enhanced customer experience (Krumm, 2011; Lundström,
2008; Müller et al., 2011a; Schaeffler, 2008). Digital signage
displays have the advantage over static signs because they can
display the multimedia content such as images, animations,
video and audio. The content can be adapted in real time to a
different context and audience (Bauer and Spiekermann, 2011),
making it attractive for use at airports, hotels, universities,
retail stores and various outdoor public spaces. However, the
displayed content is frequently generic and uninteresting for
observers causing the effect of Display Blindness (Müller et al.,
2009). To make digital signage more effective as an information
interface, the displayed content should be informative, dynamic
and attractive.
The actual attention that people pay to public displays is
one of the key parameters of digital signage. The comparative
case study of Huang et al. (2008) reveals that paying attention
to public displays is a complex process, which depends on
several criteria such as positioning of the display, display
size, content format and content dynamics. Therefore, to
maximize the attention to digital signage, these parameters
should be considered already during the design phase of the
digital signage system. Research in digital signage today is
aimed at exploring designs and options for delivering engaging
and interactive content in public places. Michelis and Müller
(2011) introduced Audience Funnel, a framework for audience
interaction generalization. Various interaction modalities are
proposed, including body position, speech, facial expression,
body posture, gaze and touch (Müller et al., 2010). Chen et al.
(2009) describe a prototype system for interaction with digital
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2 R. Ravnik and F. Solina
Object
segmentation
Face detection
and tracking
SVM classifier
SVM classifiers
Dwell time
Face registration
and orientation
In-view time
Gender
Age group
Attention time
Captured
video
Figure 1. Scheme of computer vision enhanced digital signage system.
signage using hand gestures. Also, adaptive and interactive
digital signage is permeating urban life and architecture
(Kuikkaniemi et al., 2011) as well as ubiquitous computing
(Krumm, 2011). However, ubiquitous monitoring (Moran and
Nakata, 2010) can lead to negative responses. Little and Briggs
(2009) address the problem of receiving personal information
in public spaces via personalized interaction. Grobelny and
Michalski (2011) focus on digital signage content design. By
performing pairwise human preferences comparison analysis,
they show that different layout structures, background and
content positioning can significantly affect the user’s perception
of the digital signage content.
Digital signage can yield a remarkable impact in commerce.
A generalization study by Burke (2006, 2009) reveals that
in-store digital signage increases customer traffic and sales.
Indeed, the shoppers are the most responsive to messages
that relate to the task at hand and their immediate interest. A
qualitative study using questionnaires by Dennis et al. (2010)
shows that digital signage is an effective stimulus, adding to
positive perceptions of the mall environment, the emotions and
the approach behaviour. Finally, digital signage screens also
improve the image of shopping malls and create a favourable
shopping atmosphere (Newman et al., 2010).
Digital signage, clearly, is a strong contributor in various
processes and fields; however, most of the measured impact
was observed using qualitative methods. Nearly, all reported
studies collected data by using interviews and questionnaires.
Some of these publications address the inherent limitation
of such approach and propose further research to determine
whether people’s actual behaviour really reflects their stated
beliefs (Müller et al., 2009; Newman et al., 2010). Therefore,
envisaging a quantitative method, with means for determining
various audience measurement metrics could open a completely
new window to digital signage, allowing for maximum
interaction and continuous context awareness.
In this paper, we present a computer vision-enhanced digital
signage system for monitoring the actual activity of the audience
in front of the system and collect quantitative data on the
audience, that is demographic metrics of a person’s dwell
time, display in-view time, attention time, gender and age
group. The use of this system enables a new methodological
approach to audience measurement in front of digital displays
which gives quantitative data on all observed customers. We
performed a quantitative field study in a real-world environment
of a clothing boutique to test the methodology as well as
the performance of the developed system. The collected data
were then used for audience analysis of customers. The outline
of the paper is as follows: Section 2 presents the audience
measurement metrics and the computer vision-enhanced digital
signage system, Section 3 elaborates the audience measurement
field study, Section 4 presents experimental results, Section 5
provides a further analysis and discussion of the results and
Section 6 gives final conclusions.
2. COMPUTER VISION-ENHANCED DIGITAL
SIGNAGE
A real-time audience measurement system was developed
for application in digital signage. The system is based on
computer vision methods for detecting and tracking persons’
faces from video. The video is captured by a digital camera that
accompanies the digital signage screen. From the video, the
system automatically computes various metrics and generates
quantitative statistics of detected persons. The following
temporal and demographic audience measurement metrics are
determined: (i) dwell time which represents the sum of all time
intervals when an observer was present in the same room or area
as the display, (ii) in-view time which represents the duration
of all time intervals when an observer was facing the display
screen (without necessarily paying attention to the screen), (iii)
attention time which is a part of the in-view time when an
observer is actually looking at the display and (iv) the gender
and age group which are demographic characteristics of each
individual customer.
Our digital signage system consists of four video analysis
modules, each designed for the determination of one of the
metrics. Figure 1 illustrates the scheme of video analysis
modules that are described in more detail below.
2.1. Dwell time
Object segmentation is used to determine the dwell time of
each observer that enters the store. We employ a background
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Audience Measurement of Digital Signage 3
(a) (b)
(c) (d)
Figure 2. Field study. (a) Typical camera image from the clothing store where we performed the field study. (b) Customer observing the shop
apparel. (c) Customer watching the screen. (d) Image of observer after object segmentation using background subtraction.
subtraction algorithm to extract foreground regions of the
captured image and define potential presence of observers.
Since the camera is static, we use a Mixture of Gaussians-
based background modelling (Bouwmans et al., 2008). Each
image pixel is characterized by its intensity value in RGB space.
The results of typical foreground subtraction are illustrated in
Fig. 2d.
The segmented regions are tracked using the Fast Match
Template algorithm supplied in OpenCV library (Bradski and
Kaehler, 2008). This template matching algorithm is adapted
for real-time video processing. The upper body part of an
observer is used as a template image. Based on the comparison
of module’s results with the results of human annotators, this
module is estimated to have an error of 10%.
2.2. In-view time
A frontal face detection algorithm is used to determine whether
observers are facing the display. We use the Viola and Jones
(2004) frontal face detector that runs in real time. The hit rate
of this face detection method is reported to be 98% (Lienhart
et al., 2003), which is suitable for our purposes in terms of
detection accuracy and speed. Using this face detector, we get
the location of all present faces regardless of their position and
scale down to the size of 20 × 20 pixels.
2.3. Attention time
The orientation of the observer’s head is the central parameter
in the determination of the attention time (when the observer is
actually looking at the display). We use the multi-view active
appearance model (AAM) method to register all detected faces.
The AAM simultaneously models the intrinsic variation in
shape and texture of the deformable visual object, a human
face in this case, as a linear combination of basis modes of
variation (Matthews and Baker, 2004). Although linear in both
shape and appearance, overall,AAMs are non-linear parametric
models in terms of the pixel intensities. Fitting an AAM to
an image consists of minimizing the error between the input
image and the closest model instance; i.e. solving a non-linear
optimization problem. The reported convergence rate of this
method is 98% (Saragih and Göcke, 2009). Using multi-view
AAM registration and estimated observer’s 3D position, we
determine the observer’s head orientation and consequently, if
the head is oriented towards the display, this denotes the person’s
attention.
2.4. Gender and age group classifiers
The demographic metric of age and gender is determined within
seven age groups: 1–14, 15–24, 25–34, 35–44, 45–54, 55–64
and over 65 years, all either male or female. We apply the
support vector machine (SVM) learning algorithm for the age
and gender classification (Moghaddam and Yang, 2002). The
FERET database (Phillips et al., 2000) was used as a learning
set for gender and age classifiers. The FERET database comes
fully annotated including facial images and the corresponding
gender and year of birth data for 856 individuals. We use the
AAM facial registration method described in Section 2.3 to
register a face and warp it to the normalized frontal form of size
50 × 50 pixels. Normalized FERET faces were used to train
SVM classifiers for gender and age. Using this approach, we
achieved 91% classification accuracy on the FERET testing set.
3. FIELD STUDY
A field study of the proposed digital signage system was
performed in a real-world environment, specifically focusing on
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4 R. Ravnik and F. Solina
(b)(a)
Figure 3. Broadcasting content of the digital signage display during the field study. (a) Static content type. (b) Dynamic content type.
the attention of the observers, i.e. their time metrics. We used a
24-inch Sony Vaio VPCL135FX/B computer display enhanced
with a Logitech WebCam Pro 9000 camera. The digital signage
system was positioned into a small clothing boutique in the
city center of Ljubljana, capital of Slovenia, with a population
of 300 000. The floor plan consisted of a main area (35 m
2
)
situated between the entrance and the cashier’s desk (see Fig. 2a)
with an additional room in the back used for changing.
We have selected for the field study on purpose a small
retail shop so that the entire retail space could be covered by a
single-camera unit. We should mention that the shop sells higher
priced sports fashion clothing and apparel, which can affect the
demographic and behaviour characteristics of the customers.
To achieve the highest attention rates of our signage display,
we optimized different criteria according to Müller et al. (2009)
and Huang et al. (2008). To optimize the position, the display
was mounted at eye level on a special shelf next to the cashier’s
desk, facing directly the entrance. For the eye-catching criterion,
the shelves immediately next to the display were filled with
small textile goods that were of immediate eye-catching interest.
To obtain data for assessment of the animated content criterion,
the static and dynamic content was displayed at the signage
display during the field study. The static content consisted of a
slide show with 20 slides shown in 10 s intervals. The slides
showed pictures of distinctive sportsmen and sportswomen
wearing attire from the shop’s assortment (Fig. 3a). The
dynamic content consisted of three video clips, which showed
various sports and entertainment situations (Fig. 3b). The slide
show and the videos were designed also to maximize the
colourful content criterion, the emotional content criterion and
the aesthetic look criterion.
3.1. Privacy aspects
Privacy-by-design (Brey, 2005; Langheinrich, 2001) as well
as privacy-by-architecture (Spiekermann and Cranor, 2009)
principles is incorporated in our computer vision-enhanced
digital signage architecture, to ensure secure and appropriate
handling with the acquired personal data. By design, all image
processing is performed by the display unit in real time,
therefore no visual records are stored or distributed over
network. The display unit discards video image immediately
after processing, storing only audience measurement metrics
that are sent to the central server using encrypted data transfer.
Using the proposed approach, we can acquire relevant audience
data and perform a generalized behavioural analysis without the
need to single out or even identify individual customers which
is typically needed for a qualitative analysis using interviews or
questionnaires.
Although the system processed video data in real time during
the field study, video was recorded solely for the purpose of
data verification by human reviewers. The manual annotation
was performed within 3 days, discarding video afterwards. All
customers in the shop were notified of the video recording, in
compliance with the national privacy legislation.
3.2. Verification of results
The field study was performed within 23 daily sessions,
consisting of a totally 214 h of video recordings. The tested
digital signage system acquired characteristics and attention
responses of 1294 people. To ensure the ecological validity
of collected data, all automatically obtained data (temporal
and demographic metrics) were manually verified by two
human reviewers. We devised a video annotation programme
for manual processing, following guidelines for effective video
annotation proposed by Chen et al. (2008). Cohen’s kappa
coefficient κ was used for the evaluation of the inter-rater
agreement (Carletta, 1996) of demographic metrics. Based on
the data collected in this study, we determined κ
gender
= 1.0 for
gender classification and κ
age_group
= 0.91 for the estimation of
observers’ age groups.
The accuracy of automatically obtained parameters was
compared with annotated data. The comparison shows that the
system performs with a high accuracy, giving gender classifier
86.6% and age classifier 77.1% classification accuracy. The
performance benchmark shows that the system is capable
of video processing at 21 FPS using two cores of Intel
Q8400 (2.66 Ghz) processor making it suitable for broadcasting
adaptive content in real time.
We performed also a Kruskal–Wallis (K–W) test to determine
the statistical significance of specific audience measurement
metrics (Spurrier, 2003). The K–W test is a non-parametric
method for testing whether measured data originate from the
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Audience Measurement of Digital Signage 5
0
40
80
120
160
200
240
1–14 15–24 25–34 35–44 45–54 55–64 65+
Number of observers
Age group (years)
Female
Male
Figure 4. Distribution of observers according to age and gender in the
field study.
same distribution. This method was chosen since it covers
general (not necessarily normal) distributions, as observed in
our extracted data.
Using manually verified data obtained from gender and age
classifiers presented in Section 2.4, the study reveals that 61%
of the acquired sample of customers were female and 39%
were male. The age distribution was as follows: 7% in 1–14
years, 10% in 15–24 years, 20% in 25–34 years, 25% in 35–44
years, 19% in 45–54 years, 12% in 55–64 years and 7% in
65+ years age group. The full presentation of age and gender
structure of the acquired sample is presented in Fig. 4.
Finally, we would like to comment that, in the pre-processing
phase, all retail personnel audience data were manually
excluded. In addition to the original data, we identified and
excluded also 12 outliers, i.e. people whose dwell, in-view or
attention time was 30 times over the mean.
4. RESULTS
The full results of the analysis are presented in Table 1. Note,
that the table summarizes the results of three general tests per-
formed for the audience metrics of gender, age group and con-
tent. Further, for dwell, in-view and attention time, the columns
present: the number of analysed observers/customers (N ), aver-
age dwell/in-view/attention time (mean), median (median) and
standard deviation of mean (SD). Results of the two-tailed K–W
test (α = 0.05) are presented with: mean rank, test result value
(H ), degrees of freedom (DF) and the representative P-value.
Table 1 shows a large standard deviation in all three time
metrics: dwell, in-view and attention time, which interestingly
implies strongly varying behaviour of shop customers. Indeed,
some people stayed in the shop for <20 s, whereas others were
there for over half an hour, which expectably results in high
standard deviation.
We next present individually each of the three time metrics.
4.1. Dwell time
The overall mean of dwell time, the time when a person is in
the same room as the display, is 144 s (see Table 1, row 14,
column 4). On average, each observer re-entered the scene 1.8
times. More specifically, the distribution of dwell times for all
observers is presented in Fig. 5.
Comparison of the mean dwell time for gender reveals that
male shoppers have a higher mean dwell time (156 s) than
women (137 s) (see Table 1, rows 3 and 4, column 4). Also,
the K–W test confirms the significant difference in mean ranks
distribution (H (1) = 4.25, P = 0.039).Age comparison shows
that the age group of 15–24 years has the mean dwell time
substantially below average (101 s when compared with average
144 s). The difference in distribution is also confirmed using
the K–W test (H(6) = 20.4, P = 0.002). Indeed, this
quantitatively confirms that the boutique aims at an older target
age group, between 25 and 55 years; which is also evident from
Fig. 4. According to the mean comparison and the K–W test
(H(1) = 1.48, P = 0.223), content type has no significant
effect on the dwell time.
Interpreting the results, we could reason that the observed
difference in the distribution of dwell time between males and
females is due to the difference in the number of short shopping
visits. Indeed, there are 51% of all females and only 44% of all
males that have dwell time <60 s.
4.2. In-view time
In-view time analysis shows that the display comes into the
field of view of an average person 4.9 times. The corresponding
average of total in-view time is 17.6 s (see Table 1, row 27,
column 4), indicating that the average person (customer) was
facing
the display for 12% of the total (dwell) time when the
person spent in the room with the display. Distribution of the
in-view time is presented in Fig. 6.
Gender comparison reveals higher in-view time for males. A
significant difference in distributions is also confirmed by the
K–W test (H(1) = 32.4, P 0.0001). No significant effect on
the in-view time is found for the metrics of age (H (6) = 6.77,
P = 0.343) and displayed content (H(1) = 0.85, P = 0.357).
4.3. Attention time
The analysis reveals that 35% of all people entering the store
looked at the display at least once, 12% looked at the display
at least twice and 6% three times or more. The corresponding
total average attention time of an average person was 0.7 s (see
Table 1, row 40, column 4). The conversion rate between people
engaging with the display and the ones not paying any attention
to it at all was 35%, which relates well with the conversion rate
of 33% reported by Michelis and Müller (2011). Distribution
of attention time is presented in Fig. 7.
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6 R. Ravnik and F. Solina
Table 1. Quantitative results of the digital signage audience measurement field study
Var. Value N Mean Median SD Mean rank H DF P
Dwell time
Gender Male 504 156 73.4 204 674.3 4.25 1 0.039
Female 790 137 60.3 193 630.4
Age group 1–14 95 148 60.1 186 631.3 20.4 6 0.002
15–24 133 101 43.7 146 521.4
25–34 258 154 64.4 222 650.6
35–44 323 138 68.7 191 648.9
45–54 251 163 86.8 206 687.6
55–64 153 157 72.1 213 691.8
65+ 81 124 67.6 158 650.4
Content Slides 665 141 58.1 193 635.1 1.48 1 0.223
Video 629 148 72.3 202 660.5
Overall 1294 144 64.8 198
In-view time
Gender Male 504 20.9 10.4 27.7 721.5 32.4 1 <0.0001
Female 709 15.6 6.88 22.6 600.3
Age group 1–14 95 17.9 8.15 26.7 654.7 6.77 6 0.343
15–24 133 14.5 6.15 19.2 595.4
25–34 258 18.9 8.87 27.1 662.1
35–44 323 16.2 7.95 24.1 629.9
45–54 251 18.7 8.20 26.1 642.8
55–64 153 20.4 10.4 26.3 697.7
65+ 81 15.7 8.55 18.3 667.8
Content Slides 665 16.7 8.1 22.7 637.6 0.85 1 0.357
Video 629 18.6 8.8 26.7 656.8
Overall 1294 17.6 8.38 24.8
Attention time
Gender Male 504 1.19 0.0 2.61 741.7 71.9 1 <0.0001
Female 790 0.42 0.0 1.19 587.4
Age group 1–14 95 2.39 0.55 4.54 815.1 37.6 6 <0.0001
15–24 133 0.70 0.0 1.41 663.6
25–34 258 0.60 0.0 1.35 638.2
35–44 323 0.42 0.0 1.19 589.7
45–54 251 0.67 0.0 1.69 647.5
55–64 153 0.68 0.0 1.73 659.9
65+ 81 0.66 0.0 1.29 660.9
Content Slides 665 0.60 0.0 1.49 625.8 5.71 1 0.017
Video 629 0.86 0.0 2.27 670.4
Overall 1294 0.72 0.0 1.91
Values of mean, median and standard deviation are given in seconds.
Interestingly, males are more attracted to digital signage than
females: 48% of all males and only 27% of all females looked at
the display at least once. The overall average attention time for
males was 1.2 s and for females 0.4 s (see Table 1,rows29and
30, column 4). Significant difference in distribution was also
confirmed using K–W analysis (H(1) = 71.9, P 0.0001).
The age group shows a strong impact on the attention time.
The K–W test shows a significant difference in distributions
(H(6) = 37.6, P 0.0001). Observing evident difference in
the mean attention time for the 1–14 age group (see Table 1,
row 31, column 4), we performed a two-tailed Steel–Dwass–
Critchlow–Fligner multiple pairwise comparison post hoc test
(Hollander and Wolfe, 1999) which confirms statistically
significant difference between the 1–14 group and all other age
groups. We believe that the reason for the youngest age group
being so distinctive is in shop goods. Retail assortment offered
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Audience Measurement of Digital Signage 7
0
25
50
75
100
0 90 180 270 360 450 540 630 720 810 900
Number of observers
Dwell time (s)
Figure 5. Distribution of dwell times for all observers.
0
30
60
90
120
0 102030405060708090100110120
Number ob observers
In-view time (s)
Figure 6. Distribution of in-view time for all observers.
0
150
300
450
600
750
900
0 5 10 15 20 25 30 35 40
Number of observers
Attention time (s)
0
10
20
30
40
50
60
70
0 2.5 5 7.5 10 12.5 15 17.5 20
Figure 7. Distribution of attention time. The outer chart shows the distribution of overall attention time for all observers. The dark column represents
the percentage of people that did not look at display at all (zero attention time). The inner chart illustrates the distribution of attention time for
observers who looked at the display at least once.
nearly only adult apparel and children therefore directed their
attention rather to the digital display.
Content type has no significant effect neither on dwell
time nor on in-view time; however, it has an effect on the
attention time (see Table 1, rows 38 and 39). The evaluation
confirms that the dynamic content draws 1.5 times more
attention than the static content. More specifically, the average
attention time increased for 43% when broadcasting dynamic
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8 R. Ravnik and F. Solina
content. The results agree well with the qualitative digital
signage observations (Dennis et al., 2010; Huang et al., 2008;
Müller et al., 2009) as well as with psychological studies on
attention capture (Hillstrom and Yantis, 1994; Remington et
al., 1992). Statistical significance was also confirmed using the
K–W test (H(1) = 5.71, P = 0.017).
4.4. Summary of analysed metrics by gender, age
and content type
Gender: gender has a significant impact on all three observed
temporal metrics. Men are more receptive for digital signage
than women, having on average higher dwell time, in-view time
and attention time (see Table 1).
Age: age has no effect on in-view time; however, it
affects dwell time and attention time. Children (1–14 years)
demonstrate the highest attention time, whereas the age group
of 35–44 shows the lowest attention time.
Content: content (static or dynamic) does not affect the dwell
and in-view time. However, broadcasting dynamic content
shows a strong increase (43%) in attention time.
5. DISCUSSION
5.1. Interaction graph analysis
To better understand how the studied metrics are interrelated,
we performed an interaction graph analysis (Jakulin and Bratko,
2004). Interaction graphs are based on entropy, which is a
measure of the uncertainty in information theory. Each node
in the graph corresponds to one of the observed metrics. The
information gain of each metric is expressed as a percentage of
eliminated uncertainty, written below the metric’s name. Edges
represent interaction between nodes as a value of their relative
mutual information. A negative interaction edge, presented
with an undirected dashed line, implies that the two metrics
provide partly the same information.A positive interaction edge,
presented with solid bidirectional arrows, indicates the amount
of novel information added by the pair of connected metrics
(Jakulin and Bratko, 2004).
The interaction graph of the data acquired in our field study
was calculated using the Orange machine learning framework
(Curk et al., 2005) and is illustrated in Fig. 8. Attention time
(as a continuous variable) was replaced with a binary class
variable looked-at that indicates whether one has looked-at the
display or not. The continuous metrics (dwell time and in-view
time) were discretized using entropy MLD discretization. To
ensure better legibility of the interaction graph, only the most
significant edges of each metric are shown.
The most important metric is in-view time that alone
eliminates 28.7% of uncertainty whether one looked at the
display or not. The second most important metric is in-view num,
indicating the number of times one had the display in view, that
Content
type
4.15%
Dwell
time
18.9%
0.81%
In-view
time
28.7%
1.89%
Age
group
4.47%
1.14%
Gender
5.27%
1.07%
–18.3%
Dwell
num
11.3%
–8.96%
In-view
num
24.3%
–17.5%
Day
0.17%
0.16%
1.98%
–10.7%
–22.8%
2.06%
1.73%
1.48%
–10.5%
0.12%
Figure 8. Interaction graph of metrics data collected in the field study.
alone removes 24.3% of class entropy. A negative interaction
edge between these two metrics (dashed line) indicates that
in-view num reduces class entropy by only 24.3 22.8 =
1.5% on its own, once we have already accounted for in-
view time. Gender alone provides 5.27% of information, but
if we account for the positive interaction between in-view time
and gender (solid bidirectional arrow), they together eliminate
28.7 + 5.27 + 1.73 = 35.7% of class entropy.
The most informative metrics are: in-view time (28.7%), in-
view num (24.3%), dwell time (18.9%) and dwell num (11.3%).
We explain this by reasoning that the longer person stays in
shop or the longer the display is in his/hers field of view,
the higher is the probability of observing the screen. There
is also a strong negative interaction between these metrics
indicating significant amount of shared mutual information
between them.
Metrics gender (5.27%), age group (4.47%) and content
type (4.15%) are also all informative. Positive interactions
between them and positive information gain imply that they
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Audience Measurement of Digital Signage 9
are inter-dependent. Thus by knowing gender and content type,
we eliminate 10.5% of class uncertainty.
The remaining metric day indicates the day of the week
when the observer visited the shop. This node was added to
the interaction graph to illustrate the validity of the proposed
analysis. We can strongly believe that the day of the week
does not correlate with the fact whether a shopper did look at
the screen or not. The interaction graph analysis confirms this
assumption since by knowing the day of the week, we eliminate
only 0.17% of class entropy.
5.2. Limitations and benefits of the proposed digital
signage system
There are several limitations but also benefits that one should
consider when evaluating the presented results. The proposed
methodology gives us detailed and measurable data on the
behaviour of all observed subjects. Therefore, the number
of customers analysed in such quantitative fashion is much
larger than the number of customers typically covered within
the framework of a qualitative analysis using interviews or
questionnaires. It does not, however, offer an explanation of
their behaviour. To fully explain and understand the social
aspects of diverse attention rates, a quantitative audience
measurement field study should be performed in parallel with
collection of qualitative data from the same population. Having
access to exact behavioural quantitative data on attention,
one can improve also the preparation of questionnaires and
interviews for qualitative studies.
An interesting result in our experiment which asks for an
explanation is that males are more attracted to digital signage
than females (Section 4.3). This finding could be explained, for
example, using interviews or questionnaires. We could also test
a hypothesis with the help of our quantitative methodology. For
example, the observed result in our field study that men were
more attracted to digital signage could be a consequence of the
topic of the displayed dynamic content, mainly sports in our
case. To test this hypothesis, we could replace the sport topics
with, for example, family activities, to see if the corresponding
attention data would change. We believe, however, that in order
to explain such observations, the analysis of customer behaviour
should go even deeper. A customer must first select an item,
then she or he can make a decision to buy it and, finally,
by paying for the item ending the whole purchase process.
When a group of customers comes in a shop these roles for
selecting, buying and paying can be distributed among different
people in the group. The explanation for the above observation
could therefore be that, in the framework of this field study,
women were on the average more involved in the selection of
clothing goods than men. Men who were part of a group of
customers could be, in the meantime, more responsive to the
whole store environment, including digital signage. Since the
selection phase in the whole purchasing process typically takes
longer than all other purchasing phases, this could also explain
the collected statistics of attention time. By analogy, in a more
technically oriented store, we would probably observe a reversal
of gender statistics.
The broadcasting of information by means of the digital
signage system in the presented field study was nearly optimal
due to the highly controlled environment. The digital signage
display and the complete broadcasting area were indoors,
ensuring constant lightning conditions. Since computer vision
is sensitive to changes in illumination, audience measurement
errors would increase in an outdoor environment. The display
was positioned on a prominent location at eye-level height
where the camera was able to cover almost the entire shop
area. All these and other parameters can hardly be optimal in
an arbitrary real-world situation and can adversely affect the
results of the proposed audience measurement system.
The performance of the proposed system also depends on
technical characteristics. Real-time video processing requires
a certain amount of processing power. The proposed system
utilizes two cores of Intel Q8400 processor which means that
the system is suitable for implementation on embedded devices.
By using more complex computer vision algorithms or more
expensive hardware components, such as infrared sensors or
multiple cameras for stereo vision, the system’s accuracy could
be improved. However, we believe that the proposed set of
computer vision methods offers a good price–performance
ratio and can operate in real time on low-priced hardware.
Selected computer vision algorithms are also copyright free,
which makes the proposed system setup suitable for commercial
deployment in large numbers.
It is probably already within technical means that camera-
equipped digital signage systems could remember and identify
individual customers by their appearance. But this would
require establishing customer databases which would run
against most privacy regulations. Although most customers
are quite willing to identify themselves through various
loyalty cards when they actually make a purchase, identifying
themselves by merely stepping into a store is a completely
different issue.
6. CONCLUSION
An advanced digital signage system is presented, consisting of
a display screen, a digital camera and audience measurement
software. Computer vision and machine learning methods were
implemented for an automatic assessment of the audience
measurement time and demographic metrics. The field study
that we performed shows that, if the environment is optimally
controlled, computer vision is at a stage where it can give fully
reliable data for audience measurement research.
The digital signage system was applied in a field study
for customer research in a clothing boutique, enabling a full
quantitative audience measurement research. The attention
time quantifier reveals that, on average, men pay attention
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10 R. Ravnik and F. Solina
to the digital signage display for 1.2 s, whereas women only
0.4 s. Age group comparison shows that attention time to
digital signage is the highest (2.4 s) in the children age group
(1–14 years) when compared with the all average attention
time of 0.7 s. Interestingly, the average attention time is the
lowest in the 35–44 years age group (0.42 s). The contents
quantifier, dynamic or static, shows that broadcasting dynamic
and not static digital signage content increases attention
time for 43%.
More generally, these results are aimed to improve the future
design of digital signage systems. The proposed architecture
can be implemented on new or existing digital signage
systems. Providing proper privacy care, it could serve as
an advanced quantitative tool for various types of audience
measurements. Additional social implications could be inferred
and a wider behavioural analysis could be performed based on
this non-invasive approach. We believe that using the proposed
methodology, detailed audience measurement analysis can be
performed continuously on all observed customers and at large
number of points in parallel which could offer new ways of
customer communication in marketing and advertisement. In
this way, future digital signage systems could automatically
adapt themselves to different types of retailers, to different
customer bases and to different localities using the type of
analysis of collected measurements which was used in this
article only post hoc. Since demographic metrics are obtained
in real time, they could be used to adapt content also in near real
time to reflect actual variations in customers during a single day.
Attention behaviour analysis and the possibility of designing
adaptive scenarios could lead to a further evolution in content
development for digital signage systems. Therefore, we plan to
study in the future what kind of feedback mechanism would be
the most efficient and simple enough for adaptive digital signage
systems. Finally, as the next steps towards maximum-impact
digital signage, the role of the display position, the display size
and the design of the adaptive content itself should be studied
carefully in the future. Additional machine learning algorithms
could be applied on collected statistics to reveal significant
customer behavioural patterns.
We plan to research in the future also different phases
and roles in the purchasing process. A single customer must
first select and decide to buy before making the purchase.
If several people are in a group that entered the store, these
roles are usually distributed among them. Can we determine
automatically from video who is the initiator, decider and
purchaser in a group that has made a purchase? How does
the digital signage system influence each phase or role in
the purchasing process? Is there a correspondence between a
person’s role in the purchasing process and its attention to digital
signage? Additional studies should explore how the interaction
design, usage of adaptive content and of different modalities
for interaction affect the observer behaviour and attention
towards digital signage. A comparative field study, using a
quantitative audience measurement system in combination
with a qualitative approach would offer additional insights
on the user’s perception of digital signage and his actual
behaviour.
ACKNOWLEDGEMENTS
This work was supported by the Slovenian Research Agency,
research program Computer Vision (P2-0214). We acknowledge
the cooperation and support of the boutique store in performing
the field study.
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