Moving object detection using background subtraction algorithm using simulink

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 06 | Jun-2014, Available @ http://www.ijret.org 594
MOVING OBJECT DETECTION USING BACKGROUND
SUBTRACTION ALGORITHM USING SIMULINK
Mahamuni P. D1
, R. P. Patil2
, H.S. Thakar3
1
PG Student, E & TC Department, SKNCOE, Vadgaon Bk, Pune, India
2
Asst. Professor, E & TC Department, SKNCOE, Vadgaon BK, Pune, India
3
Asst. Professor, E & TC Department, SKNCOE, Vadgaon Bk, Pune, India
Abstract
Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic
monitoring, human motion capture and video surveillance. How to correctly and efficiently model and update the background
model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. This work
proposes a general-purpose method which combines statistical assumptions with the object-level knowledge of moving objects,
apparent objects (ghosts) and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects,
ghosts and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits
gray color information for both background subtraction to improve object segmentation. The approach proves fast, ﬂexible and
precise in terms of pixel accuracy. The implementation of the background subtraction algorithm is done in two domains code is
written in Matlab, then using Simulink blocks sets.
Keywords—Motion detection, Background subtraction algorithm, real time, Matlab/ Simulink, Xilinx.
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1. INTRODUCTION
In construction automation, the tracking of worker location
or a moving object is important for labour monitoring,
resource management, and machine coordination. For object
tracking, a camera is often utilized to obtain information of
construction vehicle motion which can then be employed in
coordination controls. Real-time tracking of autonomous
vehicles, particularly for the control of multiple targets in
formations, still suffers from constraints imposed in
computation resources. CMOS camera is used for in a
prototypical tracking system for vehicles to detect their
color-tags. The digital camera has become a popular sensor
for monitoring and surveillance systems. By improvement of
the semiconductor technology, modern digital cameras with
high pixel number can provide greater image detail in
various applications. A digital image sensor usually utilizes
an array consists of the charged-couple device (CCD) or
complementary metal oxide semiconductor (CMOS). By
passing light through the lens and color filter array (CFA),
the real image is transformed into the mosaic-like RGB
image projected on the digital image sensor array. The raw
RGB image then is reconstructed as a meaningful image for
human perception by de-mosaicking and color correction.
For sensing applications, the additional image processing to
extract object is required for every image pixel. Moreover, it
should be noted that imaging processing consumes a lot of
memory space and most of computation resources in a
computer system.
The moving object detection is an important task in the
image sequences of the area which is under surveillance.
Detection of moving object is necessary for surveillance
application, for guidance of autonomous vehicles, for
efficient video compression for smart tracking of moving
object, remote sensing, image processing, robotics, and
medical imaging. The moving object detection is the initial
step in object recognition. The aim of moving object
detection is at extracting moving objects that are of interest
in video sequences with back-ground which can be static or
dynamic. The algorithm mostly uses either temporal or
spatial information in the image sequences to perform
moving object detection, and the most commonly used
approach is pixel intensity. The algorithms for moving
object detection have been proposed in the literature, most
of them can be categorized into one of the three most
popular approaches. Some of them can perform moving
object detection in real-time, they include background
subtraction. There are basically two problems one is
processing speed and second is reliability of the moving
object detection, and also the measure of quality related
algorithm of two major indexes. The moving object
detection can be done into three categories, including optical
flow method, frame difference method and background
subtraction method. And each one has its own advantages
and disadvantages [1].
Nowadays the real-time information is very important in the
surveillance area such as in military reconnaissance, mobile
robot navigation, path planning the motion detection is very
important thing. So in proposed system the digital camera is
used for tacking the video of the area. Basically there are
three techniques which are used for the motion detection in
the sequence of the images such as temporal difference,
optical flow and background subtraction algorithm. Out of
these background subtraction algorithm is used. This
algorithm will subtract the background from the frame to get

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the moving object in the frame. The idea of background
subtraction is to subtract or difference the current image
from a reference background model. First of all, establish a
reliable background updating model based on statistical and
use a dynamic optimization threshold method to obtain a
more complete moving object.
2. LITERATURE SURVEY
The video surveillance has long been in use for monitoring
security sensitive areas for examples banks, department
stores, traffic monitoring on highway, public places which
are crowded. Due to the advanced technology the large
capability of storage devices are available.
2.1 Motion Detection Methods
The motion detection methods are classified according to the
method of finding moving object [1].
Different motion detection methods are described as
follows:
1) Temporal differencing: The Frame differencing method
uses the two or three adjacent frame based on time series
image to subtract and gets difference images, its working is
very similar to background subtraction after the subtraction
of image it gives moving target information through the
threshold value. This method is [3,4] simple and easy to
implement, and also it is similar to the background
subtraction. But this method is highly adaptive to dynamic
scene changes , however, it generally fails in detecting
whole relevant pixels of some types of moving objects.
Additional methods need to be adopted in order to detect
stopped objects for the success of higher level are
computationally complex and cannot be used real-time
without specialized hardware.
2) Background subtraction: it is particularly a commonly
used technique for motion segmentation in static images. It
will detect moving regions by subtracting the current image
pixel-by-pixel from a reference background image that is
created by averaging images over time in an initialization
period. The basic idea of background subtraction method is
to initialize a background firstly, and then by subtracting
current frame in which the moving object present that
current frame is subtracted with background frame to detect
moving object. This method is simple and easy to realize,
and accurately extracts the characteristics of target data, but
it is sensitive to the change of external environment, so it is
applicable to the condition that the background is known[4].
3) Optical flow: The optical flow method uses the motion
target of the vector characteristics which changed with time
to detect motion area in image sequences. It gives better
performance under the moving camera, but this algorithm is
very complex and complicated computation and also it
needs special hardware support, so it is difficult to meet the
requirements of real-time video processing [7].
3. SYSTEM DESIGN
In this paper, Moving object is described using background
subtraction algorithm. Motion detection methods are
basically a process which detects the object in the
surveillance area [1].
The moving object in the frame sequences is detected using
background subtraction. For motion detection, two images
preferably of the same size are taken from video. In that one
image is initialised as the background image in which the
moving object is not present and the second image is the
current image. And each image has two models one is the
foreground and background model. The Fig.1 shows the
system block diagram.
Fig. 1 System Block Diagram
The foreground model is the model in which the moving
object is present and background model is the model in
which the moving object is not present. The first process for
motion detection is image initialisation. Image initialization
is process that initializes the background image. For
example, in the video the number of the frames with respect
to the time, out of these frame one is initialized as the
background image by tacking some assumption. Hence
initialization of background is essential preprocessing
operation for motion detection. And the preprocessing is
done on each frame and the preprocessing is done by the
mean filter for reducing the noise from the image. After the
preprocessing the frames are given to the background
subtraction algorithm. That subtracted image is then
segmented using Thresholding. The background subtraction
algorithm is design in the Matlab using Simulink block sets.
3.1 Software Design
In this paper, background subtraction algorithm is used for
the detection of motion in the surveillance area. The Fig. 2
shows the flow of the background subtraction algorithm
based motion detection process which consists of the video
as the input. Here for tacking video CMOS sensor camera is
used.
1) Video to frame conversion: Video is nothing but the series
of frames with respect to time[2]. Videos are image
sequences over the time. A video is a function of x, y f (x, y,
t) over time t. The camera will take continuously capture the
images. So directly processing on the video is not easy it is
difficult. So the conversion of the video into frame is done.
It will give the frames at different time period. The Fig. 3
shows how the video is converted into the frames. Once the
frames are ready then the processing is done on them.

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Fig. 2 Flow of background subtraction algorithm
2) Pre-processing: After the video to frame conversion pre-
processing is done on each frame to reduce the noise which
is present in frame. The pre-processing is done using the
mean filter, convolution filter, median filter [6,1]. The mask
of the filter will multiply with the frame and noise will get
removed so that the result is accurate. Due to this only the
background or foreground objects are preset and unwanted
factors in the frame presents at the time of the capturing the
images like dust.
3) Background frame initialization: Now after the pre-
processing is done the background frame initialization is
very important. Once the background frame is initialized it
will referred as the reference frame. There are many ways to
obtain the initial background image. For example, take the
first frame as the background directly, or the average pixel
brightness of the first few frames as the background or using
a background image sequences without the prospect of
moving objects to estimate the background model
parameters.
4) Background subtraction: After the background frame is
initialized the subtraction of the current frames and the
reference frame is done for the moving object detection [1-
5]. The subtraction will be done pixel by pixel ( , )x y of the
both frames. The simple version of this scheme where a
pixel at location ( , )x y in the current image ( , )f x y is
marked as foreground if
| ( , ) ( , ) |f x y B x y > dT (1)
is satisfied where Td is a predefined threshold. The
background image B is updated by the use of an Infinite
Impulse Response (IIR) filter as follows:
1 (1 )B I Bt t t     (2)
It is marked as the foreground frame. The above equations
gives the foreground pixels and the updated background if
the background is changes as the object is moving from one
point to another then the updating the background is
necessary. So that updated background is given to the
reference frame. Then the subtraction of the current frame
and the reference frame is done. And that subtracted image
is given to the segmentation [8].
5) Segmentation: After the subtraction of the frames the
subtracted image is then segmented using the threshold
value. That value is practically set. The segmentation can be
done as follow
Dk (x, y) = 1 for |Fk (x, y) – Bk-1 (x,y)| > T
0 for others (3)
The above equation gives the segmentation after the
subtraction of the current and reference frame. That
subtracted frame gives the subtracted value of the each pixel
that pixel value is compared with the threshold value if the
subtracted pixel value is greater than the threshold value
then it will represented by the 1 and if not greater then
represented by 0. In image processing the value of 1 is black
and 0 is white. So the segmented image gives the moving
object in white and the background is black. It will detect
the moving object in the frame.
6) Morphological filtering: The segmented frame is now
given to the morphological filtering for reducing the noise.
The function of the morphological filtering is removing the
small regions probably created by noise, fill up unnecessary
cavities, smoothing boundaries, extracting edges. It will give
pixel level operations.
Fig. 3 Spread out and shrinking image region
The above Fig.3 shows the how the morphological filter will
shrinking and growing shape of the circle.
7) Motion detection: After the segmentation and
morphological filtering the moving object is clearly seen in
the frame and that will be the output of the our system which
is display on the VGA monitor.

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3.2 Simulink Software Model for Motion Detection
The Simulink model shown in Fig. 4 is prepared in matlab
with the help of Simulink library browser which contains the
Image and Video processing block set. The software model
for motion detection consists of Image from file, Video
Viewer, Embedded function block, parameter blocks. Both
the input images are displayed using the video viewer block.
Each time we run the Simulink model, the different image
dataset has to be selected.
1) Image From File Block: This block is basically used to
access the two input images which are to be subtracted. By
double-clicking on this block, we have to select the path
where the two input images are stored. Two input clock
images of size 256 × 256 are taken as input images. Each
time to run the model for different dataset; we have to give
the path of input images.
Fig. 4 Simulink Software for Motion Detection
2) Video viewer: This block is used to display the clock
images in matlab. The input images, input image 1 and input
image 2 are displayed using this block. The output clock
image is also displayed using this block.
3) To Workspace: This block is used to store the clock
images in matlab workspace for further processing, where
we get the details of image such as size, pixel information
etc.
4) S Function Block: Embedded function block for motion
detection using background subtraction algorithm. This
block is also called as S-function block, where we actually
do the scripting by embedding the code for the designed
algorithm. This block is programmed using matlab
commands. The subtracted clock image is then displayed
using Video Viewer block.
4. EXPERIMENT RESULTS
In this experiment we use the background subtraction
algorithm for the detection of the moving object in the
surveillance area. The demonstration system has the set up
for the implementation of proposed system in the Matlab
software.
Here the reference image is initialized in the code and then
the subtraction of the current frame is done. And after the
subtraction of the both frame the subtracted image is display
on the screen. The following figure shows the reference
image, current image and the subtracted image. The
subtracted image shows the moving object in the white
colour and the background is black. The coding is done in
Matlab and the following results are seen. In the Matlab
coding the initialization of the web camera of lab-top is done
and it will captured the video. That video gets subtracted
from the reference image. The reference image is nothing
but the background image in which the object is not present.
The Fig 5(a) shows the reference image which is initialized
as the background image. This image is referred as
background image because there is no any moving object. In
the real time extraction the background image processing
approach is used to provide the most complete feature
dataset, but it is extremely sensitive to dynamic scene
changes due to lighting and extraneous events. Background
image is not fixed and it must adapt to motion changes like
tree branch move, changes in background because of objects
entering in a scene, stay for longer period without motion.

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(a) (b)
(c)
Fig. 5 (a) Reference image (b) Current image (c) Output
image
The Fig 5(b) shows the current frame which is subtracted
from the reference image. In the current frame the moving
object is present. So when the subtraction of the reference
and the current is done the moving object is detected in the
final image. In the current frame more than one moving
object is present and those moving objects are also detected
by this algorithm. It will also detect the some insects which
are present in the image
The Fig. 5(c) shows the output image in which the moving
object is detected. The subtraction of the image is done pixel
by pixel so the background of the current image and the
reference image has the same pixel value due to this
subtraction of the background pixel is zero so it will be
represented in the black colour and the moving object is
represented by white colour.
So at the end the output image is the binary image. After the
binary image getting the morphological filtering is applied
to that image and it will perform the operation like opening,
closing, sharpening the edges and it will also remove the
noise from that frame.
5 CONCLUSIONS
In this work, Background subtraction algorithm based
motion detection using a Matlab coding, Simulink. The
software reference model of the chosen architecture is
developed in MATLAB/ Simulink. Improving segmentation
results as well as being able to extract additional information
such as frame deference, background subtraction allows for
improved object detection and thus tracking. The subtraction
of the two image is gives the good results of the moving
object in the surveillance area. The resultant subtracted
frame contains the information or data from both the input
frames. It provides an effective way of detecting moving
object. It will gives better information of the moving object
in video as compared to another algorithms.
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