TRAFFIC-SIGN RECOGNITION FOR AN INTELLIGENT VEHICLE/DRIVER ASSISTANT SYSTEM USING HOG

Computer Science & Engineering: An International Journal (CSEIJ), Vol.6, No.1, February 2016
DOI:10.5121/cseij.2016.6102 15
TRAFFIC-SIGN RECOGNITION FOR AN
INTELLIGENT VEHICLE/DRIVER ASSISTANT
SYSTEM USING HOG
Karthiga.PL1
S.Md.Mansoor Roomi2
, Kowsalya.J3
1
PG Student, Department of Electronics and Communication Engineering, TCE, Madurai
2
Assistant Professor, Department of Electronics and Communication Engineering, TCE,
Madurai
3
UG Student, Department of Electronics and Communication Engineering, TCE,
Madurai
ABSTRACT
In order to be deployed in driving environments, Intelligent transport system (ITS) must be able to
recognize and respond to exceptional road conditions such as traffic signs, highway work zones and
imminent road works automatically. Recognition of traffic sign is playing a vital role in the intelligent
transport system, it enhances traffic safety by providing drivers with safety and precaution information
about road hazards. To recognize the traffic sign, the system has been proposed with three phases. They
are Traffic board Detection, Feature extraction and Recognition. The detection phase consists of RGB-
based colour thresholding and shape analysis, which offers robustness to differences in lighting situations.
A Histogram of Oriented Gradients (HOG) technique was adopted to extract the features from the
segmented output. Finally, traffic signs recognition is done by k-Nearest Neighbors (k-NN) classiﬁers. It
achieves an classification accuracy upto 63%.
KEYWORDS
Intelligent Transport System, Road Sign Recognition, RGB-based colour thresholding, HOG, k-NN
Classifier
1. INTRODUCTION
Traffic-sign recognition (TSR) is an fundamental component of a driver assistance system
(DAS)[6]. It enhances safety by notifying the driver of speed limits or possible dangers such as
imminent road works or pedestrian crossings. The traffic signs have two utmost properties are
their ‘Colour’ and ‘Shape’ using which the real-time system becomes capable to detect and
identify the Traffic Signs e.g., red-rimmed circular prohibition signs, triangular warning signs,
and blue information signs. The simpliﬁed pictograms make them easily perceivable and
understandable.
The detection and recognition of these traffic signs may face one or more of the following
difficulties: (i) Faded signs(ii) The presence of obstacles in the scene(iii) Similar objects in the
scene or similar background color (iv) Damaged signs. To overcome these difficulties many
techniques have been suggested.

16
(a) (b)
(c) (d)
Fig. 1. Examples for difﬁculties facing the TSR task: (a) Faded signs, (b) Similar objects in the scene or
similar background color, (c) The presence of obstacles in the scene, (d)Damaged signs.
S. Maldonado et al. [1] used HSI color space for detection and then used SVM for classification
of traffic signs. L.W.Tsai et ale [2] proposed a different color model for making road sign colors
more squeezed. After the transformation, the RBF network is then used to excerpt road sign
candidates and then a rectification process is also applied to rectifying each slanted road.
X.W.Gao et ale [3] applied the color advent model CIECAM97 to extract color information and
extract the shape features by FASTS model to segment and classify the traffic signs. S. Prieto et
al. [4] used a kind of ANN called SAM at every level of the detection and recognition of RSs. A.
de la Escalera et al. [6] used a inherent algorithm for the detection step, agree to invariance
localization, scale, rotation, weather conditions, partial occlusion. Then the classification is done
by using neural network classifier.
There are many methods to excerpt features from road signs for classification. P. Paclik et al. [8]
use proposed the scale change invariant normalized central moments and compactness from
binary images. H. Fleyeh et al. [9] has summarized Zernike moments as features. A fuzzy
ARTMAP is trained directly with features or by using KLT for dimension reduction, or by using
LDA algorithm as data separation and dimension reduction algorithm
In this paper the goal is to propose a new intelligent transport system to identify the red road signs
that could be used for intelligent vehicles. In these vehicles an image of road scene is captured by
a camera and then the type of traffic sign is determined. The system has three major steps: Traffic
board detection, feature extraction, and classification.
2. CHARECTRISTICS OF ROADSIGN
Traffic signs have been designed so that they are easily distinct from driving environment.
The colour for traffic sign are chosen in a way such that, it serves different purposes and is also
distinguishable for the driver while driving. The signs are characterized by fixed shapes like
triangle, circle, octagon, and rectangle. The combined feature of shape and colour are used by the

17
driver to distinguish a traffic sign. Hence an autonomous system also uses the same principle of
the colour and shape of the traffic signs. With respect to the road the traffic signs are positioned at
well-defined locations so that the drivers can expect the position of the sign. The road
sign may contain text as a string of characters, picture or both to represent the meaning of
the sign. They are characterized by using fixed text fonts and character heights. There are a large
number of traffic signs in India categorized as WARNING, COMPULSORY, REGULATORY
and INFORMATORY . This makes a total of 92 traffic signs all together. These signs are mainly
characterized by colour and shape. Figure 2 shows the different types of Indian traffic sign ,
Fig. 2 Different category of traffic signs
3. PROPOSED APPROACH
Our proposed approach uses the HOG feature and k-NN classifier to efficiently classify the
traffic-sign images. It enhances the classifier performance and improve the performance under
various illumination conditions like under or over illumination. Fig. 2 shows the proposed
approach.
Figure 2: Proposed approach
TRAINING STAGE TESTING STAGE
RGB-COLOUR BASED
THRESHOLDING
RGB-COLOUR BASED
THRESHOLDING
SHAPE ANALYSIS SHAPE ANALYSIS
FEATURE EXTRACTION FEATURE EXTRACTION
FEATURE DATABASE CLASSIFICATION
TRAFFIC SIGN
RECOGNITION

18
3.1. RGB-BASED COLOUR THRESHOLDING
Colour thresholding is a common technique in traffic sign detection and there are different
methods. Since most traffic signs contain red or blue colours, thresholding techniques concentrate
mostly on these two colours. One technique is to make use of Normalized RGB (RGBN) colour
space, presented in (Gomez-Moreno, Maldonado-Bascon, Gil-Jimenez, & Lafuente-Arroyo,
2010). This is used to decrease the effects of illumination changes and also allows to find the
correct thresholds. The mask equations for red and blue colours are the following


 ≤−≥≥
=
otherwise,
),(),(/),(and),(),(and),(),(r,
),(Re 1
False
ThRjigjirjigjibjirjigjiifTrue
jid


 ≤≥
=
otherwise,
),(/),(and),(),(b,
),(Blue 1
False
ThBjibjigjirjiifTrue
ji
(1)
Where ThR1 and ThB1 threshold values for red and blue band, these values are computed by
using binary thresholding method.
(a) (b)
(a1) (b1)
Figure:3 (a) Original image (a1) Original image (b) Detected Traffic Sign Board image
(b1)Detected Traffic Sign Board image
3.2. Shape Analysis
The main task of shape analysis includes cleaning the binary image from noise and Small objects,
applying connected components labelling algorithm and identifying the traffic sign. This module
recognized road traffic sign so that it becomes invariant to the in-plane transformations. This
means that the consequential sign has a fixed size and it is located in a standard position where its
center of gravity is located in the center of the image.

19
(a) (b)
(a1) (b1)
Figure 4 (a) original image (a1)original image (b) Results of shape analysis (b1)Results of shape
analysis
3.3. HOG FEATURE EXTRACTION
The HOG was proposed by Dalal and Triggs [12] and primarily used for pedestrian detection.
Fig. 5 shows the structure of the HOG descriptor. The image is divided into overlapping blocks.
Each block, in turn, is divided into non overlapping cells. The gradient orientation and magnitude
are computed for each pixel. A histogram of these orientations is formed for each cell. The
magnitude of the gradient is used as a vote weight. A local contrast normalization of the blocks
improves the pedestrian detection results. The resulting histograms are concatenated to form the
HOG descriptor.
Figure 5: structure of HOG descriptor
The gradient magnitude and orientation is given by,
+
(2)

20
Where Gx is the image filtered by horizontal mask and Gy is the image filtered by vertical mask
(a) (b)
(a1) (b1)
Figure6:(a) Original image (a1)Original image(b) HOG feature extracted image (b1)HOG feature extracted
image
3.4 K-NEAREST NEIGHBOR CLASSIFIER
The idea in k-Nearest Neighbor method is to identify k samples in the training set whose liberated
variable x are similar to u and we use these k samples to classify this new sample into a class v. If
all we are prepared to assume is that f is a smooth function, a practical idea is to look for samples
in our training data that are near (in terms of the independent variables) and then we have to
compute v from the values of y for these samples.
Figure 7:k-NN classifier approach
When it talk about neighbors it suggest that there is a distance or dissimilarity measure that it can
compute between samples based on the independent variables. For the moment it would concern
ourselves to the most popular measure of distance: Euclidean distance.
The Euclidean distance between the points x and u is

21
d (x,u) = ∑=
−
n
i
yixi
1
)( 2
(3)
It examine other ways to measure distance between points in the space of independent analyst
variables when we discuss clustering methods. The simplest case is k = 1 where we have to find
the sample in the training data which is closest (the nearest neighbor) to u and set v = y where y is
the class of the nearest neighboring sample.In the proposed approach traffic signs are classified
by using k-NN classifier.In this approach using 18 different classes like(school zone,one way,two
way,speed breaker etc..,) are used.
4. EXPERIMENTAL RESULTS AND DISCUSSION
4.1 Sample Database Images
A database comprising of 200 images is created for evaluation of the proposed algorithm. The
dataset consists of all variety of traffic signs like warning sign, prohibitory sign, information sign
and mandatory sign. The images are captured under varying scale and illumination conditions. .
Some of the traffic sign images are shown in figure 8.
Figure 8: sample database images
4.2 Experimental Results
In this section, the exhaustive experimentation of the proposed methodology on the created
dataset has been presented. From the experimental analysis it is observed that the proposed
methodology works better results for traffic sign recognition. The proposed methodology based
on HOG feature is experimented for 18 different classes like school zone,40 km speed limit,50
km speed limit, pedestrian crossing, speed breaker, two way crossing and one way crossing etc..,
Initially, the traffic sign board is detected from the input images using RGB based color
segmentation algorithm. After color segmentation, the segmented image undergoes a post
processing step using morphological operations. Next, the Histogram of oriented gradients of the
segmented image is computed which yields five orientation angles as theta=0, 45, 90, 135 and

22
180 degree. Finally the histogram of the edge descriptor is the feature vector of the proposed
algorithm. The obtained feature vector is classified using the traditional k-NN classifier. The
proposed algorithm for traffic sign recognition attains a classification accuracy of 63%.
Graphical User Interface (GUI) results of the proposed algorithm is shown in Figure 9,
(a)
(b)
Figure 9 : (a) Results of 50 km speed limit sign image (b) Results of school zone image
5. CONCLUSIONS
This paper proposes a new and effective method for both detection and recognition of traffic
signs. The proposed system uses a very simple and accurate detection procedure and classifies the
detected image with a accurate feature extraction algorithm along with a classifier. The proposed
strategy comprises three main sections. At first the road sign image must be detected from road
scene image. The RGB-based colour thresholding was used for detection. Then for extraction of
features using HOG. Finally the feature vector of road sign is classified using k-NN classifier.
Experimental results indicate that the proposed system is strong against non-uniform
illuminations, scale and rotations. But it has some defects. For instance if there is more than one
red road sign in the image, the response will not be unique. So some improvements remain as
tasks for the future. Hence the proposed recognition procedure could be used also for recognition
of the blue road signs and as the future task we'll improve our system to detect and recognize the
road signs in complex background scenario and we will increase the number of road sign classes.
REFERENCES
[1] S. M . Basc6n , S. L. Arroyo , P. G . Jirnenez.H . G . Moreno,F.L.Ferreras,( June 2007 )"Road-Sign
Detection and Recognition Based on Support Vector Machines" , IEEE Transactions on Intelligent
Transportation Systems , vol. 8, no . 2,
[2] L. W . Tsai , J . W . Hs ieh , C. H . Chuang, Y . J . Ts en g, K. C. Fan , C . C.Lee , H. Poor(2008 ) ,
"Road sign detection using eigen colour ", lET Comput.Vis .,Vol. 2, No .3, pp. 164-177.
[3] X.W . Gao , L. Podladchikova, D. Shaposhnikov , K. Hon g, N.shevtsova,(2006) ,"Recognition of
traffic signs based on their colour and shape features extracted using human vision models" , J . Vis
.Commun . Image R. 17 675 -685 .

23
[4] M . S. Prieto , A. R. Allen (2008)," Using self-organising maps in the detection and recognition of
road signs " , Image and Vision Computing.
[5] C .Y . Fang , C .S. Fuh , P.S. Yen, S . Cherng , S.W . Chen (2004)," An automatic road sign
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[6] A. Escalera, J .M . Armingol, M . Mata(2003) ,' Traffic sign recognition and analysis for intelligent
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[7] J .Sheeba Rani , D.Devaraj , R . Sukanesh, (2007 )" A Novel Feature Extraction for Face
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[11] Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., & Lafuente-Arroyo, S.(2010). Goal
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[12] Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection”, IJCV,
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AUTHORS
PL.Karthiga received her B.E.degree from Alagappa Chettiar College of
Engineering and Technology, Karaikudi in 2014 and is pursuing her M.E.
Wireless Technology at Thiagarajar college of Engineering, Madurai.
S. Mohamed Mansoor Roomi received his B.E.degree from Madurai Kamaraj
University, in 1990, his M.E. degree in Power Systems and Communication
Systems from Thiagarajar College of Engineering in 1992 and 1997 and his
Ph.D.in Image Analysis from Madurai Kamaraj University in 2009. He has
authored and co-authored more than 150 papers in various journals and conference
proceedings and numerous technical and industrial project reports.
J.Kowsalya pursuing her B.E.Electronics and Communication Engineering at
Thiagarajar college of Engineering, Madurai.

TRAFFIC-SIGN RECOGNITION FOR AN INTELLIGENT VEHICLE/DRIVER ASSISTANT SYSTEM USING HOG

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