Outdoor Context Awareness Device That Enables Mobile Phone
Users to Walk Safely through Urban Intersections
Jihye Hwang
1
, Younggwang Ji
2
, Nojun Kwak
1
and Eun Yi Kim
2
1
Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
2
Internet & Multimedia Engineering, Konkuk University, Seoul, Korea
Keywords: Guidance System, Mobile Phone User, Outdoor Context Awareness, Support Vector Machine, Multi-scale
Classification.
Abstract: Research in social science has shown that the mobile phone users pay less attention to their surroundings,
which exposes them to various hazards such as collisions with vehicles than other pedestrians. In this paper,
we propose a novel handheld device that assists mobile phone users to walk more safely outdoors. The
proposed system is implemented on a smart phone and uses its back camera to detect the current outdoor
context, e.g. traffic intersections, roadways, and sidewalks, finally alerts the user of unsafe situations using
sound and vibration from the phone. The outdoor context awareness is performed by three steps: pre-
processing, feature extraction, and context recognition. First, it improves the image contrast while removing
image noise, and then it extracts the color and texture descriptors from each pixel. Next, each pixel is
classified as an intersection, sidewalk, or roadway using a support vector machine-based classifier. Then, to
support the real-time performance on the smart phone, a multi-scale classification is applied to input image,
where the coarse layer first discriminates the boundary pixels from the background and the fine layer
categorizes the boundary pixels as sidewalk, roadway, or intersection. In order to demonstrate the
effectiveness of the proposed method, some real-world experiments were performed, then the results
showed that the proposed system has the accuracy of above 98% at the various environments.
1 INTRODUCTION
When walking through an urban area, we can easily
observe pedestrians talking or reading and sending
text messages on mobile phones. A number of
studies on pedestrians crossing streets have shown
that mobile phone users exhibit less safe behavior
than other pedestrians, as their attention has been
directed away from the external environment and
they do not see the overall context (Institute of
transportation engineers, 2004; Kate, 2010; Leena,
2012; Mark, 2009; Rovert, 2010; Tianyu, 2012 ).
The mobile phone users pay less attention to
their surroundings, which exposes them to various
hazards such as collisions with people, cars, and
other obstacles. Among the numerous hazards
outside, traffic intersections are the most dangerous
areas for pedestrians. Approximately 20.3% of all
traffic accidents occur at intersections and this
pedestrian accident rate is one of the highest rates
for all traffic accidents (Jack, 2007; Julie,2006).
During last decades, a number of solutions for
safety crossing the traffic intersections has been
proposed and implemented (Brabyn, 1933; Barlow,
2003; Dragan, 2011; Karacs, 2006; Volodymyr,
2008; Chen-Fu, 2012; James, 2013). Then, the initial
research has been focusing the development of the
safety system for the people with visual
impairments. For example, the audible pedestrian
signals (Barlow, 2003) and talking signs (Brabyn,
1933) have been developed in order to inform the
visually impaired pedestrians to know when to cross
intersections. However, although these solutions are
being adopted more widely, they are still only
available in limited places and additional devices
should be installed or used.
As an alternative to such systems, some vision-
based systems such as Crosswatch (Volodymyr,
2008), Zebralocalizer (Dragan, 2011) and Walksafe
(Tianyu, 2012) have been proposed to prevent the
pedestrians of the collisions with vehicles at urban
traffics. Crosswatch is a handheld vision system that
provides real-time feedback to the user about their
526
Hwang, J., Ji, Y., Kwak, N. and Kim, E.
Outdoor Context Awareness Device That Enables Mobile Phone Users to Walk Safely through Urban Intersections.
DOI: 10.5220/0005664705260533
In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pages 526-533
ISBN: 978-989-758-173-1
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
orientation and location at the crosswalk; then, it can
detect the crosswalk using edge information.
Similarly, the Zebralocalizer identifies pedestrian
crossings (i.e. zebra crossings) using line analyses
and localization of crosswalks using a camera and
3D accelerometers. These vision-based methods
function successfully on localizing crosswalks at
intersections and guiding users to cross intersections
safely.
Recently, some commercial products such as
“Text and Walk,” and “Walk and Email,” which
displays the road situation captured from back
camera on application background, thereby making
pedestrians users write SMS and e-mail while
walking safety. However, it was shown in (Ophir,
2009) that the users may not be aware of dangers
even if they are displayed as application
background. In (Sivaraman, 2010), authors
presented a car detection system based on Harr
features that exploits the back camera of
smartphone. It was well worked on the resource
constrained smartphone, however, it can only detect
cars when they were very close to the pedestrians,
limiting the time for the pedestrians to react safety.
On the other hand, none of them have considered
the current situations where a user stands on. In real
scenarios, the people require different guidance
solutions according to the context where they are
currently located. For example, if a user is at an
intersection, they want to locate the crosswalk.
However, when the user is walking on the sidewalk,
the system leads the user to walk on the far side
from the road. Accordingly, the outdoor contexts
where the user is located should be first recognized.
In this paper, a novel method for automatically
recognizing a user’s current context is proposed in
order to increase pedestrian safety, particularly for
users who operate their mobile phone while walking.
Here, the context refers to the type of place where a
user is standing, which is classified as a sidewalk,
roadway, or intersection. Among these types of
contexts, the discrimination between a sidewalk and
an intersection are more important than recognizing
a roadway.
As a key in discriminating outdoor contexts, the
orientation of the boundaries between sidewalks and
roadways are used: horizontally oriented boundaries
are found in images corresponding to intersections
and more vertically oriented boundaries are
observed in images corresponding to sidewalks.
Therefore, localizing such boundaries from input
images should be undertaken first. In order to
separate the boundaries between sidewalks and
roadways from other lines, and then to discriminate
such boundaries as sidewalks or intersections, the
color and texture properties of the images are
considered and machine-learning based
classifications such as a support vector machine
(SVM) are used. Then, in order to improve the
computation cost and accuracy, a multi-scale
classification is adopted, where a coarse layer first
classifies the boundary pixels from the background
and a fine layer classifies the boundary pixels into
one of the three contexts: sidewalks, intersections, or
roadways.
In order to evaluate the effectiveness of the
proposed system, numerous videos were collected
from real environments, and they were used to
measure the accuracy of the proposed system. From
the experimental results, it was found that the
average accuracy was 98.25%.
2 SYSTEM ARCHITECTURE
We propose a novel assistive device that aids mobile
phone users walking and crossing roads more safety.
The proposed system is implemented on smartphone
and uses its back camera to detect users’ current
context, and notifies the recognized results to users
through sound and vibration from the phone.
Then, as a key element of discriminating
between sidewalks and intersections, the orientation
of the boundaries between the sidewalks and
roadways are used. Fig 1 illustrates some sample
intersections and sidewalks captured from outdoors,
where the vertical and horizontal lines in the
boundaries between sidewalks and roadways can be
easily observed. The images corresponding to
intersections have horizontal boundaries, as shown
in Fig. 1(a), whereas the images corresponding to
sidewalks have some boundaries that are close to
vertical and non-horizontal lines (see Fig. 1(b)).
(a) (b)
Figure 1: Some of outdoor images (a) images categorized
to intersections (b) images categorized to sidewalks.
Based on these observations, the proposed
method was designed and developed. As seen in Fig.
1, it is critical to accurately localize the boundaries
from the input images. For this, we use the color and
textural properties, which are trained by machine
learning algorithm.
Outdoor Context Awareness Device That Enables Mobile Phone Users to Walk Safely through Urban Intersections
527
Figure 2: System overview.
The core algorithm to discriminate users’ current
contexts as sidewalk, roadway and intersections is
based on computer vision techniques. Computer
vision processing is a computational intensive
process that can easily drain the computational
resources and batteries of smartphone. To address
this, the proposed system use the simple image
features such as colors and textures, and use the
learning-based recognition model that is first trained
offline and then uploaded and used for the outdoor
situation recognition, as shown in Fig 2.
To build a learning based recognition model (the
right side of Fig 2), we first prepare a dataset
containing positive and negative images, for
example, images that show the sidewalk, roadway
and intersection (see Fig 1). Such sets of images are
first preprocessed to enhance the contrast while
filtering some noises, then input to an algorithm that
extracts characterizing features such as colors and
textures and then used to build classifiers able to
determine if a picture contains sidewalk or traffic
intersection. The resulting classifiers are then used
by the smartphone application running on
smartphones, running the online context recognition
in real-time.
In on-line scenarios, the proposed system is
performed by three steps: preprocessing, feature
extraction, and context recognition, which are
described in the left side of Fig 2. In preprocessing,
some noise and contrast are removed and improved
using Gaussian smoothing and histogram
equalization. Then, the useful visual features are
extracted including the color and textures; saturation
and intensity are used as color descriptors and a
histogram of oriented gradient (HOG) is used to
describe the textural properties. These features then
become the input for the context recognition
module, which separates the boundaries from the
background and re-categorizes them as pixels
corresponding to sidewalks or intersections. In this
study, multi-scale classification is used to reduce the
computational cost of the classifier and improve the
accuracy.
3 FEATURE EXTRACTION
As a key element of discriminating the outdoor
contexts, the orientation of the boundaries between
sidewalks and roadways are used. In order to
discriminate the boundaries between sidewalks and
roadways from other lines, the visual characteristics
such as color and texture are investigated. Here, the
saturation and intensity are used to describe the
color and a histogram of oriented gradient (HOG) is
used to describe the texture.
The color information is used as inputs for
classification problems. In this study, the RGB
image is converted to the HSI color model using
below equation, because the HSI color model
represents colors in a similar manner to that which
human eyes sense them.
Generally, the pixels that correspond to the
roadway have distinctive color information, i.e.
these regions have lower color saturations than
others, as half of the color belongs to roadways that
are marked with gray. Accordingly, the saturation
and intensity are used to discriminate these contexts.
Then, the saturation and intensity for every M×M
sized sub-region is computed as the average of each
quarter sub-region.
In order to describe the textural properties of
sidewalks and intersections, various texture
descriptors have been considered such as the
histogram of oriented gradient (HOG), fast Fourier
transform (FFT) coefficients, and wavelet transform.
Through the experiment, the HOG method was
selected as the most reliable method to describe the
textural properties.
The HOG is a feature descriptor that counts the
occurrences of a gradient orientation in the sub-
regions of an image, and it is often used for object
detection. This method is computed on a dense grid
of uniformly spaced cells and uses overlapping local
contrast normalization for improved accuracy.
In this paper, the contexts are defined as
sidewalks and intersections; these two contexts have
different characteristics in terms of the gradient
orientations. The boundaries of sidewalks are
oriented close to vertical, and the horizontally
oriented boundaries were found in the images
corresponding to intersections. However, other
image parts corresponding to the same objects have
a relatively uniform gradient orientation. Therefore,
ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods
528
the HOG is effective in both separating the
boundaries between sidewalks and roadways from
other images, and categorizing the contexts.
For all M×M-sized sub-regions, the HOGs are
calculated using the following procedures. First, the
gradient magnitude and orientation of each pixel are
computed using the Sobel operator; then, each
pixel’s gradient orientation is discretized into six
histogram bins, and the pixels’ magnitudes are
accumulated in the corresponding HOG bin. Finally,
the HOG is normalized.
Fig 3 presents HOG characteristics for some
different types of regions. Fig 3(a) shows the HOG
distributions for the sub-regions corresponding to
the sidewalks and intersections, and Fig 3(c) shows
those for parts of some objects. Interestingly, in such
figures, distinctive distributions are seen according
to their region type. Generally, for the sub-regions of
the sidewalks and intersections, the distribution of
the HOGs tends to be biased at specific histogram
bins. However, other regions have relatively
uniformly distributed HOGs. In other words, the first
two histograms have larger variances than the last
one.
(a) (b) (c)
Figure 3: Examples of HOGs (a) and (b) HOGs
distributions of sub-regions corresponding to the
sidewalks and intersections, (c) HOGs distributions of
other sub-regions. (row: HOG’s bin).
4 CONTEXT RECOGNITION
In this module, the current context where a user
stands is recognized based on the colors and
textures. As mentioned above, the key elements for
context recognition are the boundary orientations
between sidewalks and roadways: horizontally
oriented boundaries are found in images
corresponding to intersections and more vertically
oriented boundaries are observed in images
corresponding to sidewalks. Accordingly, the
boundary should be first discriminated from other
natural lines; then, these pixels should be classified.
In the proposed method, such classifications are
accomplished using a multi-scale classification,
which is illustrated in Fig 4.
Figure 4: The multi-scale classification scheme.
The multi-scale classification applies the same
operator to the input image while changing their
scale and this method has been used in numerous
applications including image segmentation and
classification (Zoltan, 1996). The proposed multi-
scale classification mechanism is composed of a
coarse layer and a fine layer: the coarse layer
discriminates the boundary pixels and the fine layer
categorizes the pixels into sidewalk and intersection
pixels. Then, in order to accomplish the different
goals of the respective layers, the SVM is used as a
classifier.
4.1 Locating the Boundary Pixel in the
Coarse Layer
Generally, a boundary is detected using the edge
operator and Hough transform. However, the
boundaries detected using these methods include
many sections that correspond to natural lines as
well as boundary pixels, which increases the
computation costs for the classification process. In
the coarse layer, each boundary pixel is classified as
a boundary pixel from other regions.
As shown in Fig. 4, the original image is first
downscaled by 20×20, thus the average value of
input pixels within a 20×20 block is assigned to the
pixel in the down-sampled image. Then, for each
pixel and its neighbors, the color and texture
properties are extracted.
Thereafter, they input into the SVM-based
classifier. The SVM was originally a binary
classification algorithm developed by Vapnik et al.
and it has been successively extended by numerous
other researchers (Crammer, 2010).
Given a set of training examples that were
classified as boundary or other, the SVM training
algorithm builds a model that assigns new data into
one category or the other. In this paper, the Gaussian
radial basis function (RBF) was used according the
experiment.
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Outdoor Context Awareness Device That Enables Mobile Phone Users to Walk Safely through Urban Intersections
529
For the experiment, the parameters were set as
follows: gamma = 1 and cost = 1000.
Fig 5 shows the results of boundary extraction.
For the input image in Fig 5(a), the result is shown
in Fig 5(b), where the boundaries are denoted using
the white color. As can be seen in the figure, the
SVM training algorithm can successfully separate
boundaries from other natural lines.
4.2 Determining the Context of the
Boundary Pixel in the Fine Layer
In the fine layer, the original image sized at 640×480
is used. Accordingly, every boundary pixel expands
to a block with a size of 20×20, each pixel of which
has been categorized as intersection, sidewalk, or
roadway by the SVM-based classifier.
As is well known, the SVM is used to classify
objects into two class types. Accordingly, in order to
to determine the current context from the three
possible contexts, i.e. sidewalk, roadway, or
intersection, two SVMs are used hierarchically, as
follows:
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(2)
The first SVM classifies a pixel’s class as
sidewalk or other, and then the second SVM
discriminates a pixels class as either an intersection
or a roadway. Then, the same visual features that are
used in the coarse layer are exploited.
(a) (b) (c) (d)
Figure 5: Examples of the multi-scale classification
results. (a) input image, (b) boundary extraction results,
(c) context classification results, (d) final results after
post-processing.
Fig 5 presents the outdoor context recognition
results. For the input image in Fig. 5(a), the context
classification result is shown in Fig. 5(c) where the
red pixels and blue pixels are used to denote the
pixel class as a sidewalk or intersection,
respectively. As shown in Fig. 5(c), the
classification results have some errors (noise)
because the classification decision is performed
locally on each pixel. Thus, smoothing is performed
globally on the texture classification results in order
to combine the individual decisions for a whole
image. In order to solve this problem, a grid map
was designed where each cell has a size of 80×80.
The grid map is overlaid on the classification results,
and then the classification decision for each cell is
made. Then, the context of a cell is determined by
the majority rule. For example, if most pixels in a
cell are classified as intersections, the cell is also
considered to be an intersection. Fig 5(d) shows the
final classification result where the red block and
blue block denote the sidewalk and intersection
classes, respectively.
5 EVALUATION
In order to assess the practical validity of the
proposed system, a number of real-world
experiments were performed. The 30-minte walking
experiments were asked to participants from 10:00
to 18:00 hours at six different locations. The
smartphone was held by the participants standing at
the streets. We recorded the captured by a phone,
and counted the number of the respect context that
appeared in the video as ground truth, which were
used for objective evaluation of the proposed
system.
Three females and two males participated in the
evaluation and their average age was 25.6 years. In a
day, all participants used mobile phone 6 hours on
average and usually were used mobile phone on the
road to sending text message, playing the mobile
phone game and so on. In addition, three participants
had the accident experience with person or poles that
stay in front of the intersection while using a mobile
phone on the road.
Each participant was given an introduction on
how to operate the proposed system. Furthermore,
the participants were asked to walk using the
instructions provided by the proposed system. If a
participant arrives at intersections on their way, they
should stop and wait for the traffic signals. Then, we
took participants to unfamiliar traffic intersections to
test the system.
At the selected locations for real-world
experiments, the accuracy of the proposed context
recognition was evaluated. We compared the results
with the ground truth. The average results are shown
in Table 1. The overall accuracy was above 98%.
ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods
530
Table 1: The accuracy of proposed system.
Location 1 2 3 4 5 6 Total
Accuracy 100 98.4 100 94.4 100 96.7 98.25
Fig 6 some recognition results for various
environments. Fig 6(a) shows the input images,
where the images have time-varying lighting and the
sidewalks have diverse patterns and colors. The
input images were first enhanced in the
preprocessing stage, and then the classifications
were performed.
(a) (b) (c) (d)
Figure 6: Context recognition results. (a) Input image, (b)
boundary extraction results, (c) classification results, (d)
final results after post-processing.
As shown in Fig. 6(b), the boundaries between
the sidewalks and roadways were correctly
extracted, despite the diverse patterns on the
sidewalks; however, they still included some false
classifications. Fig. 6(c) shows the classification
results, and Fig 6(d) shows the final results after
post-processing. Although some cells were
misclassified, the majority of the cells were
classified as intersections; thus, its context was
considered as an intersection. All examples
recognized the correct context even though some
pixels on the boundaries were misclassified. The
results demonstrate that the proposed method has
robust performance in the ground pattern and
illumination type.
Fig 7 presents some examples of errors that
occurred in complex scenes. As seen in Fig. 7, most
boundary sections between the sidewalks and
roadways were concealed by pedestrians and their
shadows, so the boundaries in the fine layer
classification could not be extracted (see the first
row of Fig 7(b)). Thus, two images were
misclassified as sidewalks. However, these errors
can be easily resolved if the history of the place
types between specified time frames are used.
(a) (b) (c) (d)
Figure 7: Examples of misclassified images by moving
walkers. (a) Input image, (b) boundary extraction results,
(c) classification results, (d) final results after post-
processing.
The primary purpose of the proposed system is to
increase the safety of pedestrians who use their
mobile phone while walking. For the system’s
practical use as an assistive device, real-time
processing should be supported. The average
processing time was approximately 304.16ms. As
such, the proposed method can process more than
four frames per second on resource-constrained
smartphone. Consequently, the experiments
demonstrated that the proposed method produces
superior accuracy for context awareness, thereby
assisting safer navigation for pedestrians in real-
time.
In order to study the participants’ satisfaction in
using the proposed system, the participant
satisfaction when using the proposed system was
investigated. The following four questions were used
to determine the participant satisfaction.
z E1: How helpful the system when you were
walking?
z E2: How difficult was it to understand the
system?
z E3: Is it comfortable to use the proposed
system in the real context (How much it is
comfortable)?
z E4: Do you want to use this system if it is
developed as smart device app?
Outdoor Context Awareness Device That Enables Mobile Phone Users to Walk Safely through Urban Intersections
531
Fig 8 presents the results of the participant
survey where the middle line represents the standard
deviation. As seen in the figure, most participants
were satisfied with the proposed system.
Figure 8: The user survey results.
In question E1, there was 76% satisfaction with
the proposed system. Participant 5 stated, “I usually
fall down while walking, but if I used this system, I
could prevent a dangerous situation.” Although the
proposed system sometimes recognized incorrect
information, the accuracy for detecting intersections
was 96%, and participants could avoid most
dangerous situations.
Question E2 referred to the ease of
understanding and using the proposed system. The
proposed system sent a warning alarm to the
participant when they were approaching a traffic
intersection and made them stop. Participant 1 said,
“Because I was using the system for the first time, I
was confused when I stopped. However, after
practice I could use the system well without any
effort.” Most participants were slightly confused by
the proposed system when they used it for the first
time, but gradually their skill with and
understanding of the proposed system improved
with use without greater effort.
In question E3, all participants felt that it was
convenient to use the proposed system. The
participants received information that analyzed the
current context using an audio or tactile interface.
Sometimes they complained about the earphones,
because they blocked other outside sounds.
Participant 2 stated, “Usually I use earphones to
listen to music, so I thought that the alert sound
‘beep’ was just noise.”
In question E4, approximately 80% of the
participants agreed that if the system were developed
on a smart phone as multitasking, they would use it.
Participant 5 said, “Actually, now I really need this
system. I always use my smart phone while walking,
and I don’t see intersection quickly enough. So, if
this system was developed as multitasking, I will
download it.” Consequently, in future research, the
proposed system will be developed for multitasking
smart devices.
Consequently, the experiments demonstrated that
the proposed system produces superior accuracy for
context recognition and that participants feel
comfortable in using the proposed system.
6 CONCLUSION
In this study, an assistive device that increases the
safety of the pedestrian, particularly for users who
use their mobile phone while walking, was
presented. The primary goal of the proposed system
is to automatically recognize outdoor contexts such
as intersections, sidewalks, and roadways, thereby
reducing the number of traffic accidents involving
pedestrians and vehicles. The proposed system is
composed of three modules: pre-processing, feature
extraction, and context recognition. In order to
reduce the computational cost and improve the
accuracy, a multi-scale classification was used
where a coarse layer discriminated the boundary
pixels and a fine layer categorized the pixels into
sidewalks and intersections. In both layers, the
support vector machine was used as the classifier.
In order to assess the validity of the proposed
system, real-world experiments were conducted with
five participants. The results demonstrated that the
proposed method could recognize outdoor contexts
with an accuracy of 98.25%, which proved the
practical validity of the proposed system as mobility
aids for pedestrians. To fully support the safety of
pedestrians, obstacle detection and avoidance should
be embedded into the current system. In future
research, this will be extended to the current system.
ACKNOWLEDGEMENTS
This research was supported by Basic Science
Research Program through the National Research
Foundation of Korea(NRF) funded by the Ministry
of Science, ICT & Future Planning(NRF-
2013R1A1A3013445).
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