1. Traffic Sign Recognition for Advanced Driver
Assistance System Using PCA
Kande Prasyam1
, S. Himabindu2
1
PG Student, Department of Electronics & Communication Engineering, ASCET, Gudur, A.P, India.
2
Asst. Professor, Department of Electronics & Communication Engineering, ASCET, Gudur, A.P, India
1
499prasyam@gmail.com
2
bindu437@gmail.com
Abstract:
Traffic sign Recognition plays a vital role
for the drivers in order to avoid the hurdles like speed
breakers, narrow bridge or even accident zone etc.
This paper presents the effective recognition of traffic
signs using Principal component analysis. This could
be done by placing a camera which captures the road
sign images and it will be displayed as a video file in
the GUI. This video file is converted into frames
called array indexing. Here this technique uses
different methods of image processing such as image
segmentation, sign recognition and sign classification.
The Eigen values of these images calculated and given
to LPC 2148 processor where it will be interfaced
with the audio amplifier and shows the sign direction
in LCD.
Index Terms---- Road sign, Principal Component
analysis, Graphical user interface, Eigen values,
Eigen vectors, LPC 2148.
I. Introduction
Image itself a matrix, it will be arranged in the
forms of Rows and columns. For comparing of the
similar images Independent Component analysis is
sufficient but for comparing different images with
different Eigen values principal component
analysis came into picture. Here the Eigen values
are calculated and are compared with the database
values so that how close the value matches that
would be treated as image for sign recognition.
The primary objective of this paper is to
extract the details of type of sign that exists in sign
board and intimates to the driver through the voice
alert. The other advantage is the voice alert is in the
language of the driver.
II. RELATED WORKS
This paper focuses on the image processing
modules and hardware modules where for
interfacing between them RS232 cable is used.
The software module consists of three modules
such as image segmentation, sign recognition
and sign classification and hardware
components such as LPC 2148, audio amplifier
and LCD Display.
A. Image Processing module:
This module by name itself indicates that
processing of image using different techniques
such as RGB Colour segmentation,
Recognition of signs and classification of
them.
Image segmentation:
Initially sign board images are captured
using camera and can be segmented in order to
determine the exact boundaries of that image
used for effective analysis. This colour
segmentation is used to be converted to 2D
image and then calculate the Eigen values
easily
Sign Recognition:
After segmentation of image the sign is
recognised from the sign board used for
advanced driver assistance. These recognized
signs given as an input to the classification
module.
Sign Classification:
The classification stage includes
compares the signs that are recognised and
with the database images.
Proceedings of International Conference on Developments in Engineering Research
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2. Block Diagram:
Fig 1. Block Diagram of image processing
module
The sign board images gives as an input to
the personal computer where it processes the image
and compares the image with the database images
and finally selects the image that is closer to
database images. The extracted image after
comparing with the database images will send to
the RS232 cable.
This RS232 cable sends the index number
to LPC 2148 that is generated from Personal
computer using Keil software. ARM processor
interfaces with the audio amplifier as well as
displaying the type of sign in LCD.
Eigen Values and Eigen Vectors:
Eigen vectors are non-zero vectors of a
linear operator and result in a scalar multiple of
them when operated on by the operator. The scalar
then called the Eigen value where In association
with the Eigen vector. The property of matrix is in
which when a matrix acts on it only the vector
magnitude is changed but not the direction.
Consider the Eigen vector of X where A is a vector
function
AX=λX (1)
By using equation 1 we get equation 2
(A-λI)X=0 (2)
where I is the n x n Identity matrix.
The above mentioned is a homogenous system of
equations and we know that a non trivial solution
exists only when
det (A-λI) = 0 (3)
Where det() indicates the determinant and
this above equation is also known as characteristic
equation of A. If A is nxn , then there are n
solutions or n roots of the characteristic
polynomial. This characteristic polynomial is of
order n. Hence there are n Eigen values of A
satisfying the equation.
AXi=λXi (4)
Where i=1,2,3,….n
If all the Eigen values are distinct, there are n
associated linealy independent eigenvectors, whose
directions are unique, which meant for an n
dimensional Euclidean space.
Eigen Sign Approach:
The Eigen values of the input signs
captured is compared with the database signs. If
any matching occurs with the database image then
accordingly based on the index number it will
shows the turn right, turn left, turn curve etc., based
on the assignment of the index value to the
corresponding sign.
When the Sign image to be recognized
may be known or unknown that is captured by a
camera and we get the weights associated with the
Eigen signs, that linearly approximate the sign or
can be used to reconstruct the sign. Now these
weights are compared with the weights of the
known sign images that are available in database
so that it can be recognized as a known
sign.The Euclidean distance between the image
projection and known projections is calculated; the
sign image is then classified as one of the signs
with minimum Euclidean distance.
Mathematically calculations:
Let a sign image I(x,y) be a two
dimensional N by N array of (8-bit) intensity
values. An image may be considered as a vector
Sign
board
images
PC RS232
LPC
2148
Audio amplifier LCD
Display
Speaker Indications
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3. of dimension N2
, for a typical image it will be a
256 by 256 size and would be the vector of
dimension 65,536 or equivalently we will say that
65,536-dimensional space. An ensemble of images,
then, maps to a collection of points in this huge
space. Principal component analysis would find the
vectors that best account for the distribution of the
sign images within this entire space.
Let us consider a set of sign images be
T1,T2,T3,….TM. This sign images data set has to be
mean adjusted before calculating the covariance
matrix or Eigen vectors. The average sign is
calculated as Ψ = (1/M) Σ1
M
Ti, Each image in the
data set differs from the average sign by the vector
Ф = Ti – Ψ.This is actually mean adjusted data. The
covariance matrix is
C = (1/M) Σ 1
M
Φi Φi
T
(5)
= AAT
where A = [ Φ1, Φ2, …. ΦM].
The covariance matrix considered here is a
N2
by N2
matrix and would generate N2
eigenvectors and eigenvalues. It is impractical to
calculate with image sizes like 256 by 256, or even
lower than that.
An effective solution is needed to
calculate the Eigen vectors. Set of images that are
considered is less than the no of pixels in an image
(i.e M < N2)
, then we can solve an M by M matrix
instead of solving a N2
by N2
matrix. Consider the
covariance matrix as AT
A instead of AAT
The eigenvector vi can calculated as follows,
AT
Avi = μivi (6)
where μi is the eigenvalue. Here the size of
covariance matrix would be M by M.Thus we can
have m eigenvectors instead of N2
. Premultipying
equation 6 by A, we have
AAT
Avi = μi Avi (7)
The right hand side gives us the M Eigen signs of
the order N2
by 1.All such vectors would make the
image space of dimensionality M.
As the accurate reconstruction of the sign
is not required, we can now reduce the
dimensionality to M’ instead of M. This is done by
selecting the M’ Eigen signs which have the largest
associated Eigen values. These Eigen signs now
span a M’-dimensional subspace instead of N2
. A
new image T is transformed into its Eigen sign
components.
wk = uk
T
(T - ψ) (8)
where k = 1,2,….M’.
The Euclidean distance of the weight
vector of the new image from the sign class weight
vector can be calculated as follows,
εk = || Ω – Ωk|| (9)
where Ωk is a vector describing the kth sign
class.Euclidean distance. The sign is classified as
belonging to class k when the distance εk is below
some threshold value θε. Otherwise the face is
classified as unknown. Also it can be found
whether an image is a sign image or not by simply
finding the squared distance between the mean
adjusted input image and its projection onto the
face space.
ε2
= || Ф - Фf || (10)
where Фf is the face space and Ф = Ti – Ψis the
mean adjusted input.
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4. Using these we can say whether the image as
known sign image, unknown sign image and not a
sign image.
B. Interfacing Module:
In generally most of the personal
computers are provided with two serial ports and
one parallel port. A parallel port sends and receives
data bits very faster nut the required number of
wires is more whereas for serial communication it
will send one bit at a time through the single wire
and hence slower but the required number of wires
are less.
RS232 is meant for serial communication
transmission of data in order to connect the DTE
(Data Terminal Equipment) and DCE (Data
Communications Equipment). For example,
connecting a computer terminal with the printers,
modems, UPS and other peripheral devices.
RS232C is the latest one where RS232 is
Recommend Standard number and C is the latest
revision of the standard. It specifies that 25-pin D
connector and most of the PCs are equipped with
the male type D-connectors consists of only 9 pins.
III. Hardware Module:
After the selection of image based on the
index value obtained from RS232 cable is given as
an input to the hardware such as LPC2148. This
LPC2148 will be interfaced with both the audio
amplifier and LCD display.
A. LPC 2148:
ARM7 is most widely used in embedded
system application such as ranging from mobile
phones to automotive braking systems. The number
of transistors used in ARM7 is fewer which reduce
the costs and power consumption. The ARM7 is
based on a 16bit/32 bit with real-time emulation
and embedded trace support. This also provided
with the 512 kilobytes of embedded high speed
flash memory.
This LPC2148 also consists of 128-bit
wide memory interface and unique accelerator
architecture which will enable 32-bit code
execution at maximum clock rate.
Block Diagram:
Fig 2. Block diagram of typical sign recognition system
B. Liquid Crystal Display:
It is very thin and flat panel used for electronically
displaying information such as text, images as well
as moving pictures. It has enormous applications
include monitors for personal computers,
televisions, instrument panels, and other devices
ranging from aircraft cockpit displays to every-day
consumer devices such as gaming devices, clocks,
watches, calculators, and telephones.
Hardware Circuitry:
Fig 3. Before displaying the type of sign
Image
acquisition Pre-
processing
Feature
Extractor
Sign image
Normalized
sign image
Training sets
Classifier
Sign database
Feature vector
‘known’ or
‘unknown’
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5. Fig 4. Voice alert in accordance with the type of sign
displayed in LCD
C. Audio amplifier:
It is an electronic device that increases the
strength of audio signals that pass through it. Audio
amplifies up to the level that is suitable for driving
loudspeakers. The different amplifiers that exist are
car audio amplifier, PC audio amplifier, TV audio
amplifier etc., and can be chosen based on the
application. Finally this audio amplifier output is
given to the speakers.
IV. Experimental Results:
The input images are captured by camera
where it will be processed and compared with the
database images. If any matching occurs then the
corresponding sign image will be displayed and
simultaneously the type of sign is also displayed at
the bottom of the GUI.
This would be done for various sign
images and voice alert also provided with respect to
the sign image displayed. LCD screen is also
interfaced in order to display the type of sign that is
present.
Fig 5. Traffic sign recognition results
In the above figure shown that the sign board is
displayed on the GUI as well as it will show the
type of sign in the box at the bottom. Here it will
display the Right hand curve sign image and like
manner we will process and display the image
using PCA in an effective way.
V. Conclusion:
This paper provides an effective
recognition of traffic signs using PCA algorithm
and thereby providing the voice alert to the drivers
as well as display. After processing of the input
sign image in GUI, then the allotted index number
in accordance with the sign image is given to
RS232 cable. Based on the index number the
corresponding sign image direction will be audible
in the speakers and displayed on LCD module. In
the future we may expect the same feature of traffic
sign recognition without the interfacing of GUI
module with the LPC2148 using RS232 cable in
TMS320CXXX in which the performance also be
increased.
References:
[1] Prof. V.P. Kshirsagar, M.R.Baviskar, M.E.Gaikwad, ” Face
Recognition Using Eigen faces”.
[2] “Facial Recognition using Eigenfaces by PCA” by Prof. Y.
Vijaya Lata, Chandra Kiran Bharadwaj Tungathurthi, H.
Ram Mohan Rao, Dr.A. Govardhan, Dr.L.P. Reddy,
International Journal of Recent Trends in Engineering,
Vol. 1, No. 1, May 2009.
[3] “Face Recognition using Eigenface Approach” by Vinaya
Hiremath, Ashwini Mayakar,.
[4] “Face Recognition using Eigenfaces and Neural Networks”
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6. by Mohd Rozailan Mamat, Mohamed Rizon, Muhammad
Firdaus Hasim, American Journal of Applied Sciences 2
(6): 1872-1875, 2006 ISSN 1546-9239, 2006 Science
Publications.
[5] L.D. Lopez and O. Fuentes, "Color-based road sign detection
and tracking", Proc. Image Analysis and
Recognition(ICIAR), Montreal,.CA, Agust 2007.
[6] ARM Data Manual sheets
http://www.keil.com/dd/docs/datashts/philips/lpc2141_42_44_4
6_48.pdf
[7] A. D. L. Escalera, J. M. A. Armingol, and M. Mata, "Traffic
sign recognition and analysis for intelligent vehicles ",
Image and Vision Computing, vol. 21, pp. 247–258, 2003.
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