A Review on Object Detecting
systems using Neural-Fuzzy approach
in MATLAB
Pratik Ghate, Prachi Chandekar, Mrs. J. S. Gawai
Electronics Engineering Department, Rashtrasant Tukodji Maharaj Nagpur University
Chhatrapati Shivaji Maharaj Administrative Premises
Ravindranath Tagore Marg Nagpur 440001, India
pratikghate29@gmail.com
jyotsna12.gawai@gmail.com
Electronics Engineering Department, Rashtrasant Tukodji Maharaj Nagpur University
Chhatrapati Shivaji Maharaj Administrative Premises
Ravindranath Tagore Marg Nagpur 440001, India
prachichandekar2605@gmail.com
Abstract— This model deals with the
background
adaption,
background
subtraction and object detection. Here
neural-fuzzy approach is used. This is one of
most important paper used for security
systems. This paper deals with one to one
organizing map and also uses artificial neural
networks for the same. This model deals with
static as well as dynamic backgrounds and is
evaluating the real time images also. This
model shows robustness against dynamic
background and detecting objects from the
videos as well as from static images. Once this
paper is setup it will continuously monitor the
area and there is no need of switching the
modes as day mode, night mode etc. as it will
be able to adapt background by itself.
Keywords— neural networks, object detection,
fuzzy logic, adaptive background, background
subtraction.
I.
INTRODUCTION
Whenever
systems, camera
we talk about security
comes into picture for
continuous monitoring and also the use of
sensors is also essential for detecting any
unwanted materials. When the continuous
monitoring process is running, there should be a
system that should adapt any kind of background
and will adapt sunlight, night darkness, rainfall,
or snowfall. In any of these conditions the
monitoring of the system should not fail and the
protection should always robust without any of
the excuse. This model provides same kind of
scenario for security systems may it be Line of
Control (LOC), big and exclusive malls, banks
where only the trusted persons are allowed to
enter [1].
II
WORKING CONCEPT
Here we are using neural-fuzzy logic for setting
threshold value and background subtraction.
The\neural networks deals with setting up the
threshold value for the selected image frame that
is extracted from the video file. The feed
forward back propagation type of neural
network is been used here and for fuzzy logic
the sobel operator is used for background
subtraction.
4)
B: Fuzzy Logic
A: Neural Networks
The first stage consist of the neural
network stage in which the image will be
extracted for the video file and the threshold
value will be setted up. As the neural network
consist of three layers i.e. the input layer, the
hidden layer and the output layer [4].
1) Input Layer: In the input layer the
input will be taken by the layer itself
from the input video one frame will
be selected and the threshold will be
calculated by the threshold layer [4].
2) Hidden Layer: In this layer the
threshold will be selected and it is
calculated.
Threshold
means
basically the mean or the average
value. It is calculated by the gray
value of the image. First the image
will be converted into gray scale
image from the RGB image. The
RGB image is of 8 bits therefore
total is 255. The 0 value is of black
and the 1 value is of white the exact
gray image will be obtained from the
value i.e. 128. First the input image
will be converted into gray scale
image and the threshold will be
decided [4].
3) Output Layer: The output layer will
compare the threshold value of the
image from the threshold value of
the video frames and if the threshold
value is greater than the original
threshold value then it is foreground
i.e. moving objects are present and
the for further processing the image
will be given to the fuzzy logic stage.
If the threshold value of the video is
lower than the calculated threshold
value then the no moving objects are
detected and it is background so the
output will be again feeded back to
the input layer again [4].
Fig 1. Block diagram of fuzzy logic
Here as we can see the fuzzy logic block
diagram is there are several blocks;
1) Input Image: The input will be given
from the neural stage for further
processing [3].
2) Image Fuzzification: Fuzzification
means converting the crisps values to
the linguistic variables. Crisps values
are the Boolean values i.e. 0 or 1 and
the linguistic variables are the once
which are used by the fuzzy logic for
its working and it stored in the Fuzzy
logic block.
3) Fuzzy Logic Block: The fuzzy logic
block consist of all the variables on
which the fuzzy logic works, it also
consist of the linguistic variables and
some other variables that are used by
the fuzzy logic for its successful
working.
4) Fuzzy Inference: It is a block in
which the subtraction of the
background will take place and in
this block we will apply all the if
conditions and if rules.
5) Expert Knowledge: In this block the
operators are present likewise we are
using the sobel operator in this
project.
6) Image Defuzzification: In this block
the linguistic variables are again
converted into crisps values for
further processing.
7) Image result: Depending upon the
presence of object the result will be
detected [3].
D: Background subtraction
C: Object Detection
Fig 3. Block diagram of background subtraction
III.
APPLICATIONS
1) This concept can be used in security
systems in big malls and multiplexes at
the night time when the malls are
closed. At ideal condition when the mall
is closed the passageway where the
camera is situated should be always
Fig 2. Block diagram of object detection
In object detection there are several
stages when he input is in the form of the video
is provided, it passes through the neural stage
where the threshold is set and depending upon
the threshold the background or foreground is
detected. Then the next stage is the fuzzy stage
where the coloured images are converted into
gray scale images and the histogram is used for
aligning the images into desired parts. Then in
background subtraction stage the background
subtraction is there depending on the values of
gray scale images after that shadow removing
process is there where the shadow is eliminated
and the gray scale image is used for the same.
After morphological operation labeling of
images is there and depending on the results the
output is obtained.
vacant not even a single animal or
human being should be present, so if
any object will pass through the vicinity
of this camera it will this model will
detect the object and an emergency
alarm will ring.
2) In Line of Control (LOC) this system
can be used for security in day as well as
in nightfall time and in any seasons. At
LOC where there should not be even a
small animal should be present, so we
can
situate
camera
and
it
will
continuously monitor that space and
even a small object can be detected. It
the changes we can setup this model at
can also be done that we can attach a
any background i.e. it can be placed
machine gun with this model and if at
inside or outside atmosphere.
all any object is detected then the
2) This technique can be used in security
machine gun will start firing irrespective
systems. This model can be successfully
of the object whether that object is
be used in security systems to perform
animal or human being.
real time monitoring of the situation and
3) It can also be used in industries to check
whether
any
unwanted
person
detect unwanted objects.
is
3) This model is easy to use and can be
entering and any unwanted object is
used by common peoples. This model is
present. In the factory environment there
very easy to handle and once it is setup
should be only limited number of
at any place it can monitor continuously
workers be present there but if at we can
without any breaks provided there is
store the data about those workers in
continuous supply of electricity. It also
such type of model using physical and
reduces man power and is very cheap.
genetic parameters and if any person or
There is just setting up cost but no
animal appears other than these then the
maintain cost.
head will be informed directly through
4) This model also has the ability to update
any communication method.
the parameters. The parameters for real
4) This system can be successfully used in
time image monitoring system. Multiple
video analysis task. We can use this
number of videos can be saved in the
model in video analysis as well as in
memory so that it can be used almost
static pictures. In real time images we
anywhere.
will have to store the ideal video in the
5)
Connection
multiple
numbers
of
memory so that the real time images that
cameras with this model as it can be
are captured are compared with the ideal
able to adapt any background changes.
video and the unwanted objects are
Due to adaptive nature there is no need
instantly detected.
of switching modes from day to night or
ADVANTAGES
1) This model deals with static as well as
dynamic backgrounds. This model can
be used in static as well dynamic
backgrounds as this model will adapt all
from night to day.
IV.
CONCLUSION
In the present scenario many
object detecting systems which are
available are working on the concept of
background subtraction and filters, but
in this paper we are proposing a new
REFERENCES
method which will work on the concept
medium. The results will be more
1. Hamidreza Rashidy Kanan and Parasto
Karimi” Visual Object Tracking Using
Fuzzy-based Thresholding and Kalman
Filter” International Journal of Modeling
and Optimization, Vol. 2, No. 3, June 2012.
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accurate as the concept of artificial
Detection Using MATLAB”Vol. 1 Issue 6,
neural network is also used.
August – 2012 International Journal of
of neural network and fuzzy logic itself.
The neural-fuzzy will itself work as a
filter as well as background subtracting
Engineering
V.
FUTURE SCOPE
achieving the following issues:
research will be done object like
animal and human beings will be
distinguished. And the distinguishing
will be based on physical as well
parameters.
will
be
Technology
3. Sen-Ching
S.
Cheung
and
Chandrika
Kamath,” Robust techniques for background
1) Object classifications When further
parameters
&
(IJERT).
Future work will be directed towards
genetic
Research
Physical
stored
and
accordingly the humans will be
classified as child, adult, man,
subtraction in urban traffic video”2011
IEEE.
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Approach
for
Adaptive
Object
Detection in Dynamic Backgrounds for
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Using Fuzzy Logic Technique”2009
IEEE.
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animals also as flying birds, water
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Veli
Mumcu,”
animals like fishes, land growing
Tarik
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recognition using moment invariants”
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Tarik
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