FENCE PROTECTION ALGORITHM USING CCTV CAMERAShsnemer.weebly.com/uploads/7/6/2/1/7621583/ramya_masterthesis.… · FENCE PROTECTION ALGORITHM USING CCTV CAMERAS Faculty of Arts

FENCE PROTECTION ALGORITHM USING CCTV CAMERAS

Faculty of Arts, Computing, Engineering and Sciences

FENCE PROTECTION ALGORITHM USING CCTV

CAMERAS

By

RAMYA TUMMURU

(18041184)

M.Sc in COMPUTER AND NETWORK

ENGINEERING

(2009-2010)

SHEFFIELD HALLAM UNIVERSITY Page | 1


ACKNOWLEDGEMENT

My heart full gratitude and thanks to God, my parents and other family

members and friends who gave me the prospect and support to complete

this project.

I wish to place on my record my deep sense of gratitude and hearty thanks to

my project supervisor, Dr. Hussein Abdul-Rahman for his constant

motivation and valuable help and also for his active interest throughout the

project work. I also extend my thanks to other Faculties for their Cooperation

during my Course.

Finally I would like to thank my friends for their cooperation to complete this

project.



ABSTRACT

The main aim of this project is protecting a fence by continuous monitoring

using CCTV cameras.

This project is basically developed to protect an area surrounded by fence

from any intruders entering into it. CCTV cameras are used to detect any

person or a moving object entering into fencing of a designated security area.

An alarm will immediately start when the camera detects a moving object

entering the fencing virtual zone. It is used for security purpose in many high

security areas to detect intruders efficiently. We have used MATLAB software

for the development of this project. Thresholding is done in order to

distinguish object of interest from its background. Binary images are created

from gray scale images. Median filter is used to remove any noise in binary

image and labeling is done. Connected components labelling scans an

image and groups its pixels into components based on pixel connectivity, i.e.

all pixels in a connected component share similar pixel intensity values and

are in some way connected with each other.

When developing this project factors like external disturbances like shadows,

wind etc are considered and are successfully solved to accomplish expected

requirements.

.



CONTENTS

TITLE 1

ACKNOWLEDGEMENT 2

ABSTRACT 3

CHAPTER 1: INTRODUCTION 10

1.1 Aim 10

1.2 Objectives 10

1.3 Research methodology 11

1.4 Activities undertaken 11

1.5 Related work 12

1.6 Scope of work 13

1.7 Structure of dissertation 13

CHAPTER 2: LIERATURE REVIEW 14

2.1 Image processing 14

2.1.1 Applications of image processing 15

2.1.2 Benefits of image processing 16

2.2 MATLAB 16

2.2.1 Mat lab syntax 17

2.2.2 Variables 17

2.2.3 Vectors /matrices 18



2.2.4 Semicolon 20

2.2.5 Graphics 20

2.2.6 Structures 20

2.2.7 Function handles 21

2.2.8 Secondary programming 21

2.2.9 Simulink 21

2.2.10 Classes 21

2.2.11 Object- oriented programming 21

2.2.12 Interactions with other languages 22

2.3 Image processing in MATLAB 22

2.3.1 Image formats supported by MATLAB 23

2.3.2 Intensity image/gray scale image 23

2.3.3 Binary image 24

2.3.4 Indexed image 24

2.3.5 RGB image 24

2.3.6 Multiframe image 24

2.3.7 Converting between different formats 24

2.3.8 Conversion between double and uint8 25

2.3.9 How to read files 26

2.3.10 Reading and writing image files 26

2.3.11 Loading and saving variables in mat lab 26



2.3.12 Loading and saving variables 27

2.3.13 Syntax for reading image 27

2.3.14 Syntax for displaying image 27

2.3.15 Syntax rgb to gray scale 28

2.3.16 To display image given on matrix form which does not require

any tool box 28

2.3.17 To display an image given in matrix form using image processing

tool box 29

CHAPTER 3: VIDEO CONTENT ANALYSIS 30

3.1 Video content analysis 30

3.1.1 Content based video retrieval 30

3.1.2 Applications of video content analysis 31

3.1.3 Professional and educational applications 31

3.1.4 Consumer domain applications 31

3.1.5 Feature extraction for content analysis 32

3.1.6 Structure analysis 33

3.2 People counting/footfall 34

3.3 Fence protection (Intruder detection) 35

3.3.1 Fence protection techniques 35

CHAPTER 4: ALGORITHMS 37

4.1 Thresholding 37



4.2 Adaptive Thresholding 39

4.3 Histogram 41

4.4 Connected components Labeling 47

4.5 Median filtering 51

4.6 Hybrid median filtering 53

4.7 Flowchart showing process how this project works 54

CHAPTER 5: RESULTS 56

CHAPTER 6: CONCLUSION 61

6.1 Conclusion 61

6.2 Future work 62

REFERENCES 63



LIST OF FIGURES:

Fig 2.1: Plotting of a sine wave function using MATLAB 20

Fig 3.1: Fence showing a virtual zone in red 36

Fig 4.1 Threshold, Density slicing 37

Fig 4.2 Adaptive thresholding or Dynamic thresholding 39

Fig 4.3 Histograms 42

Fig 4.4 Original image / Gray scale image 43

Fig 4.5 Binary image 43

Fig 4.6 Original image 44

Fig 4.7 Thresholded image 44

Fig 4.8 Threshold too low 45

Fig 4.9 Threshold too high 45

Fig 4.10 Effect of uneven illumination in single value thresholding 46

Fig 4.11 Adaptive thresholding 47

Fig 4.12: Original image 49

Fig 4.13: Result of applying geodesic operator 49

Fig 4.14: Original image 50

Fig 4.15: Thresholded image 50

Fig 4.16: Labels coded as gray values 50

Fig 4.17: Labels coded as colours 51

Fig 4.18: Labels coded as 8 different colours 51

Fig 4.19: Showing actual process of this project 54



Fig 5.1. (a) Green virtual zone as an intruder approaching the zone,

(b) Foreground objects in the scene. 56







Fig 5.5. (a) Red virtual zone as an intruder approaching the zone,


Fig 5.6. (a) Red virtual zone as an intruder approaching the zone,


Fig 5.7: The effect of shadow in the proposed algorithm. 60



CHAPTER 1

INTRODUCTION

1.1 AIM:

The main aim of this project is protecting fencing by using CCTV cameras.

Basically this project is developed to protect a designated security area from

any intruders entering into it by continuous monitoring through CCTV

cameras. CCTV cameras are used to detect any person or a moving object

entering into fencing of a designated security area. An alarm will immediately

start when CCTV camera detects a moving object entering the fencing virtual

zone. It is used for security purpose in many high security areas to detect

intruders efficiently. I have used Mat Lab technology for development of this

project. Thresholding, labeling and median filter algorithms are used for this

project. This project provides security by continuous automatic security

monitoring. This project also considers various types of fence protection

techniques and how this is better than efficient than other types of fence

protection.

1.2 OBJECTIVES:

To introduce fence protection using CCTV camera and also explain

how it is going to be implemented in the project.

To explain about image processing and how the images are monitored

using CCTV cameras.

Research into image processing techniques and algorithms used for

processing the images and identifying the intruder.

Literature survey and research into similar journals, books, articles

and websites.

Writing Mat Lab program for obtaining the result or output.



Monitoring and preventing any intruder entering the fencing using

CCTV cameras.

1.3 RESEARCH METHODOLOGY:

Research methodology is about the activity of the project. Research is

the application of systematic techniques and methods in pursuit of answers

to questions. This comprises of gathering information of books, theories,

papers and individual methods. Most of the data mentioned in this project is

been gathered by the survey. Literature survey and research are been done

using some journals, books, articles and websites etc Practical research is

been done by learning about the techniques of image processing and fence

protection.

Some of the research methods that are been followed in this project are.

Survey

Case study

Practical research.

Quantitative research

1.4 ACTIVITIES UNDERTAKEN:

This project mainly aims on how a fence can be protected from intruder by

using CCTV camera

To analyze what fence protection techniques are being used in the

present world.

Research is been done on image processing techniques and video

content analysis and also on the other techniques implemented in this

project.

A wide research has been made on image processing techniques

thresholding, labeling and using median filter to reduce the noise.



Literature survey and research in to similar technical papers, books,

articles and websites.

Learning MatLab functions for implementing and writing code.

Monitoring and controlling fence by using CCTV camera and detecting

the intruder and ringing an alarm.

1.5 RELATED WORK:

To complete the project I have done a research and survey on the following

I have studied about various fence protection techniques used in world today

from

G.Honey(1998).Electronic protection and security systems.

Chapter-3: page 111

John Fay, John J Fay(1993). Encyclopedia of security

management: techniques and technology . page 228

Robert L. O’Block, Joseph F. Donnermeyer(1991). Security and

crime prevention. Page 313.

I have studied about image processing techniques from

Rafael C.Gonzalez, Richard Eugene Woods, Steven L.Eddins (2004).

Digital image processing using matlab. Pearson prentice hall.

Ting-Chung Poon, Partha P.Banerjee(2001).Contemporary optical

image processing with matlab.

I have studied about video content analysis from

Ajay Divakaran(2008). Multimedia content analysis: theory and

applications. Page 67.

Klaus Krippendorff, Mary angela Bock(2008). The content analysis

reader.



Sagarmay Deb(2004). Multimedia systems and content based image

retrieval. Page 199.

I have studied about thresholding and labeling from

Yue Hao(2005). Computational intelligence and security:

international conference. Pg 1055

Tarek Sobh, Khaled Elleithy, Asif Mahmood(2008). New algorithms

and techniques in telecommunications, automation. Pg 193.

1.6 SCOPE OF WORK:

The project FENCE PROTECTION USING CCTV CAMERAS is used in

many high security areas like airports and banks for preventing any

intruder entering that area.

1.7 STRUCTURE OF DISSERTATION:

The report is divided into several sections and a brief overview of the section

is described here.

Chapter 2- A Brief Review. This consists of introduction about image

processing and Mat lab.

Chapter 3- Requirements and Approaches to the project. Video content

analysis, fence protection techniques and people counting(footfall) and how

it is used in security applications.

Chapter 4- This consists of methods implemented. This consists of

describing image processing techniques like thresholding, labeling and

median filter used to remove noise and actual procedure how project is done.

Chapter 5- This consists of Results and Conclusion obtained for the project.



Chapter 6- This contains Bibliography and list of web sites used.



CHAPTER-2

LITERATURE REVIEW

2.1 IMAGE PROCESSING:

Manipulating and analyzing images by using a computer is known as image

processing. These Image processing techniques were first developed in 1960

by collaboration through a wide range of scientists and academics. The

Image processing is a process which is used for converting a digital or

analog image signal which into a physical image. The output which is

obtained can be actual physical image or will be having the characteristics of

an image. The most common type of image processing is photography.

There are many wide range of image processing operations in addition to

this. The digital image processing field is developed and has created new

range of applications and tools like face recognition software, medical image

processing and remote sensing. To enhance images and to correct images

specialized computer programs are used. Algorithms are applied to the

actual data which will reduce signal distortion, adds light to an underexposed

image and clarify fuzzy images. when the image processing techniques are

first developed equipment cost and processing cost are very high. Wide

range of scientist and academics focused mainly on development of medical

imaging, character recognition and creating high quality images at

microscopic level. Costs of computing equipment dropped by 1970s which

has made digital image processing more realistic. Film and software

companies have invested funds for development and enhancement of image

processing which has created a new industry.

The image processing field grows continuously as long as speed of computer

processing increases while storage memory cost continues to drop.

The acquisition of images which produces input image in first place is

referred as imaging. Typical image processing operations are



Euclidean geometry transformations such as rotation, enlargement

and reduction.

Color corrections such as brightness and contrast adjustments,

color mapping, color balancing, quantization, or color translation to

a different color space.

Digital composting or optical composting which is a combination of

two or more images which is used in film-making to make a "matte"

Interpolation, de-mosaicing, and recovery of a full image from a

raw image format by using a Bayer filter pattern

Image recognition, the alignment of two or more images

Image differencing and morphing

Image recognition, for example, extract the text from the image

using optical character recognition or checkbox and bubble values

using optical mark recognition

Image segmentation

High dynamic range imaging obtained by combining multiple

images

Geometric hashing for 2-D object recognition with affine invariance.

2.1.1 APPLICATIONS OF IMAGE PROCESSING ARE:

Computer vision

Optical sorting

Augmented reality

Face detection

Feature detection

Lane departure warning system

Non- photo-realistic rendering

Medical image processing

Microscope image processing

Morphological image processing

Remote sensing



2.1.2 BENEFITS OF IMAGE PROCESSING:

Three major benefits of digital image processing are:

Consistent high quality of image

Low cost of processing

Ability to manipulate all aspects of the process

The Image Processing Toolbox provides a comprehensive set of reference-

standard algorithms and graphical tools for image processing, analysis,

visualization, and algorithm development. we can perform image

enhancement, image de-blurring, feature detection, noise reduction, image

segmentation, spatial transformations, and image registration. In order to

take advantage of multicore and multiprocessor computers many functions in

the toolbox are multi-threaded.

A diverse set of image types are supported by image processing toolbox

including high dynamic range, gigapixel resolution, ICC-compliant color, and

tomographic images. Graphical tools helps us to explore an image, examine

a region of pixels, adjust the contrast, create contours or histograms, and

manipulate regions of interest or ROIs.

With the toolbox algorithms you can restore degraded images, detect and

measure features, analyze shapes and textures, and adjust the color balance

of images.

2.2 MATLAB:

Mat lab stands for Matrix Laboratory and it is numerical computing

environment and is developed by math works. Mat lab is high level language

and interactive software package for engineering and scientific computation.

It offers high performance and enables us to perform computationally



intensive tasks faster than traditional programming languages like c, c++ and

Fortran.

Mat lab integrates matrix computation, numerical analysis, signal processing,

and graphics in a easy –to –use environment where problems and solutions

are expressed just as they are written in mathematical way. It allows matrix

manipulations, plotting of functions and data, implementation of algorithms,

creation of user interfaces, and interfacing with programs written in other

languages like c, c++ and Fortran. Mat lab was used by more than one

million people across the industry and academic world. The users come from

various economic backgrounds like engineering, science, and economics.

Mat lab is intended primarily for numerical computing but an optional toolbox

uses MuPAD search engine which allows access to symbolic computing

capabilities. Simulink is an additional package which adds graphical multi-

domain simulation and model based design for embedded and dynamic

systems.

MATLAB was first adopted by control design engineers but it quickly spread

to many other domains. It is now also used in education, in particular the

teaching of linear algebra and numerical analysis, and is popular amongst

scientists involved with image processing.

2.2.1 MATLAB SYNTAX :

Simplest way of executing the MATLAB code by typing it in the prompt, >>,

Command Window, will be one of the element of the desktop of MATLAB and

it can also be used as interactive mathematical shell. Extending these

available commands the sequences of command by using MATLAB editor

can be saved in a text file, as a script or also as encapsulated into a function.

2.2.2 VARIABLES:

MATLAB is a programming language which is weakly dynamically typed.

Where the variables are defined using the assignment operator, =. It can be

said as a weakly typed language because types are implicitly converted and

because variables can be assigned without declaring their type and except if

they are to be treated as the symbolic objects, and so that their type can be



changed it can be called dynamically typed. The Values can be from

constants and from the computation in which it involves values of the other

variables, or from the output of a function.

The MATLAB has many functions for rounding into integers the fractional

values:

• round(X): Rounds to nearest integer, it trails 5 rounds to the nearest

integer away from the zero

• fix(X): Rounds to nearest integer towards the zero

• floor(X): Rounds to the nearest integer towards the minus infinity. It

rounds to nearest integer less than or equal to

• ceil(X): Rounds to the nearest integer towards positive infinity. It

rounds to the nearest integer greater than or equal to X

2.2.3 VECTORS /MATRICES:

MATLAB the "Matrix Laboratory" generally refers to a 2 dimensional array

and it provides many convenient ways for creating the vectors, matrices, and

multi-dimensional arrays. In the MATLAB vernacular, a vector refers to a one

dimensional matrix, it is commonly referred as an array in the other

programming languages. In an m×n array m and n are greater than or equal

to 1. Arrays with more than two dimensions can be referred to as

multidimensional arrays.

MATLAB provides a simple way to define simple arrays using the syntax:

init:increment:terminator.

For instance:

>> array = 1:2:9

array =

1 3 5 7 9

It defines a variable named array or it assigns a new value to an existing

variable with the name array which is an array consisting of the values 1, 3,



5, 7, and 9. Which means the array starts at 1 which is the init value, and

increments with each step from the previous value by 2 which is the

increment value, and it stops once it reaches 9 which is the terminator value.

>> array = 1:3:9

array =

1 4 7

The increment value can actually be left out of this syntax along with one of

the colons to use the default value of 1.

>> ari = 1:5

ari =

1 2 3 4 5

It assigns to the variable named ari an array with the values 1, 2, 3, 4, and 5,

since the default value of 1 is used as the incrementor.

Indexing is the one-based on which is the usual convention for matrices is in

mathematics, although it is not for some programming languages.

Matrices can be defined by separating the elements of a row with using blank

space or comma and by using a semicolon to terminate each row. The list of

elements must be surrounded by the square brackets: [ ]. Parentheses: () are

used to access elements and sub arrays. Parenthesis are also used to

denote a function argument list.

Most of the MATLAB functions can accept matrices and they will apply

themselves to each element. MATLAB includes standard "for" and "while"

loops, but by using MATLAB's vectorized notation it often produces code that

is easier to read and is faster to execute. This code, when excerpted from the

function magic m, creates a magic square M for odd values of n



2.2.4 SEMICOLON:

In many other languages the semicolon is used to terminate commands, but

in MATLAB the semicolon serves to suppress the output of the line that it

concludes

2.2.5 GRAPHICS:

A Function plot can be used to produce a graph from two vectors x and y.

The code:

x = 0:pi/100:2*pi;

y = sin(x);

plot(y);

The following figure of the sine function is produced.

Fig 2.1: Plotting of a sine wave function using MATLAB

2.2.6 STRUCTURES:

MATLAB also will support the structure data types. Because all the variables

in MATLAB are arrays, the name structure array will be the most adequate in

which each element of an array will be having the same field names. It also



supports the field names which are dynamic. Unfortunately the MATLAB JIT

don’t supports the structures of MATLAB. And as a result just simple bundling

of various variables into structure will be coming at a cost.

2.2.7 FUNCTION HANDLES:

The MATLAB also support elements of the lambda-calculus which are

implemented in .m files or nested and anonymous functions by introducing

references to the functions, function handles.

2.2.8 SECONDARY PROGRAMMING

Secondary programming carried in the matlab which will incorporate the

MATLABs standard code into the more user friendly way inturn and inorder to

represent a function or system.

2.2.9 SIMULINK:

A secondary program which can be incorporated by using MATLAB is

simulink. A way to create or to collaborate Equations of the Motion by using

an infrastructure of the click, drag, and connecting the blocks. These blocks

inturn can be used in doing many things such as defining the inputs,

variables, EOMs and Scopes.

2.2.10 CLASSES:

MATLAB also will support the classes, and however the syntax and calling

conventions are different compared to other languages significantly, because

the MATLAB does not have any data types for reference. To call a method

Object method();

We cannot alter normally any variables of object variable. Inorder to create

an impression that this method alters the state of variable, MATLAB

toolboxes use evalin() command, which will have its own restrictions.

2.2.11 OBJECT-ORIENTED PROGRAMMING:



The MATLAB supports the object oriented programming including the

classes, inheritance, virtual dispatch, packages, pass-by-value semantics,

and pass-by-reference semantics.

2.2.12 INTERACTIONS WITH OTHER LANGUAGES:

MATLAB will be able to call functions and subroutines written in the c

programming language and Fortran. And allowing MATLAB data types to be

passed and also returned a wrapper function is created. The dynamically

loadable object files can be created by compiling such functions which are

termed as "MEX-files" which means matlab excecutable MATLAB

executable.

Libraries which are written in java, ActiveX or .NET can also be called directly

from MATLAB and from many MATLAB libraries like XML or SQL support and

are implemented as wrappers around the Java or ActiveX libraries. It is

more complicated to Call MATLAB from the Java, but it can be done using

the MATLAB extension or by using an undocumented mechanism known as

JMI or Java-to-Matlab Interface. It may be confused with related Java

Metadata used with a interface also called as JMI.

2.3 IMAGE PROCESSING IN MATLAB

.A digital image can be said as an instruction of how to color each pixel.

Digital image will be composing pixels which can be thought as small dots on

the screen. Generally typical size of an image is 512-by-512 pixels and it

would be convenient if dimensions of the image are let to be a power of 2.

For example, 29=512. Generally we say that if an image is composed of m

pixels in the vertical direction and n pixels in the horizontal direction then it is

of size m-by-n.

For example if we have an image of the format 512-by-1024 pixels then it

means that the data for that image must contain information about 524288

pixels and it requires a lot of memory. Hence, compressing of images is

essential for an efficient image processing. For compressing an image

wavelet analysis and Fourier analysis helps us significantly. For reducing



amount of data required to store a image there are few computer scientific

tricks like entropy coding.

2.3.1 IMAGE FORMATS SUPPORTED BY MATLAB ARE:

The below image formats are supported by Matlab:

• BMP

• JPEG

• HDF

• XWB

• TIFF

• PCX

Most widely used compression standard for images is JPEG and images

which on the Internet are mostly JPEG-images. Generally by looking at the

suffix of an image which is stored we can see what format is the image

stored in. An image which is named as myimage.jpg is stored in the JPEG

format. To start working with image or to perform any operation like wavelet

transform on the image we must convert it into a different format. We must

first read image which is stored as JPEG into mat lab. There are four

common formats to which images must be converted.

Intensity image or gray scale image

Binary image

Indexed image

RGB image

Multiframe image

2.3.2 INTENSITY IMAGE/ GRAYSCALE IMAGE:

Gray scale image is the image we work mostly. An intensity image represents

an image as a matrix in which every element has a value and according to

how bright or dark the pixel must be colored at corresponding position. To

represent the number which represents brightness of the pixel there are two

ways. The double class or data type which will assign a floating number to

each pixel which is "a number with decimals" between 0 and 1. The value 0



will correspond to black and the value 1 corresponds for white. There is other

class called uint8 which assigns an integer between 0 and 255 inorder to

represent the brightness of a pixel. The value 0 corresponds to black and

value 255 corresponds to white. Compared to the class double the class

uint8 requires only 1/8th of storage roughly. Many mathematical functions

can only be applied to the double class on the other hand.

2.3.3 BINARY IMAGE:

This image format will also store an image as a matrix but it can only color a

pixel black or white and nothing in between black or white. It will assign a 0

for the black and a 1 for white.

2.3.4 INDEXED IMAGE:

Practical way of representing the color images is indexed image. An indexed

image will store an image as two matrices. The first matrix has the same size

as the image and has one number for each pixel. The size of the second

matrix may be different from the image and it is called the color map. The

numbers from the first matrix will be an instruction of what numbers must be

used in the color map matrix.

2.3.5 RGB IMAGE:

Another format for color images is RGB image. It is an image with three

matrices of sizes matching the image format where each matrix corresponds

to one of the each colors red, green or blue. It also gives an instruction

about of how much of each of these colors a certain pixel must use.

2.3.6 MULTIFRAME IMAGE:

This format is very common in medical and biological imaging where you

study a sequence of slices of a cell. This format is very useful In applications

where we study a sequence of images. For the cases in biological and

medical imaging, the multi frame format would be a convenient way of

working with a sequence of images.

2.3.7 CONVERTING BETWEEN DIFFERENT FORMATS:



The following table shows how we can convert between different formats. all

the commands below needs an image processing tool box. We must type

name of the image which we wish to convert within the parenthesis.

Operation Matlab commandTo Convert between

itensity/indexed/RGB format into

binary format.

dither()

To Convert between intensity format

into indexed format.

gray2ind()

To Convert between indexed format

into intensity format. ind2gray()

To Convert between indexed format

into RGB format. ind2rgb()

To Convert a regular matrix into

intensity format by scaling. mat2gray()

To Convert between RGB format into

intensity format. rgb2gray()

To Convert between RGB format into

indexed format. rgb2ind()

The mat2gray command is useful if we have a matrix which represents an

image but if the values represent the gray scale range in between, 0 and

1000. This command mat2gray automatically re scales all entries such that

they fall within 0 and 255 if we use the uint8 class or 0 and 1 if we use the

double class.

2.3.8 CONVERSION BETWEEN DOUBLE AND UINT8:

When we store an image if we store the image as a uint8 image because it

will require far less memory than the double. When we performing

mathematical operations on a image or processing an image we should

convert it into a double. Conversion of back and forth between these two

classes is very easy.

I=im2double(I);

It converts an image which is named I from uint8 into double.



I=im2uint8(I);

It converts an image which is named I from double into uint8.

2.3.9 HOW TO READ FILES:

When we want to work with an image, it will generally be in the form of file. if

try downloading an image from the web, it will be usually stored as a JPEG-

file. If once we have done the processing an image, and if we may want to

write it back to a JPEG-file for that we will be able to post the processed

image on the web. This can be done using the commands imread and

imwrite. These commands will also require an image processing toolbox.

2.3.10 HOW TO READ AND WRITE THE IMAGE FILES:

Within parenthesis we must type the image file name within the single quotes

’ ‘ which we wish to read. As the first argument within the parenthesis we

must type the name of the image we have worked with.

As a second argument within the parenthesis we must type the name of the

file and the format that we wanted to write the image into.

file name must be put within the single quotes ' '.

Operation Matlab command

To Read an image imread()

To Write an image to a file imwrite( , )

After these commands be sure to use semi-colon ; or otherwise we will get

a lots of numbers which will be scrolling on our screen. These commands

imread and imwrite supports all the formats supported by the matlab.

2.3.11 LOADING AND SAVING THE VARIABLES IN MATLAB:

Once after we read a file, we probably convert it into an intensity image i.e, a

matrix and the work with this matrix. Inorder to continue to work with this

matrix again we need to save the matrix representing the image. This can be

done easily using the commands save and load which are very commonly



used matlab commands and they work independently of what tool boxes are

installed.

2.3.12 LOADING AND SAVING THE VARIABLES:

Operation: Matlab command: Save variable X . save X Load variable X . load X

Firstly download an image from web, and read it into matlab, then investigate

its format and save the matrix representing the image.

Now open the mat lab and make sure that we are in same directory as our

stored file.

2.3.13 SYNTAX FOR READING AN IMAGE:

I=imread('img1.jpg');

whos

save I

ls

Now there must be a file named "I.mat" in our directory containing our

variable I.

2.3.14 SYNTAX FOR DISPLAYING AN IMAGE:

clear

load I

whos

imshow(I)

I=im2double(I);

whos

for i=1:256

for j=1:256

Ired(i,j)=I(i,j);



end

end

whos

imshow(Ired)

2.3.15 SYNTAX FOR CONVERTING RGB TO GRAY SCALE:

A=rgb2gray(A);

whos

imshow(A)

Displaying an image in Matlab

Below are basic Matlab commands which don’t require any tool box for

displaying an image.

2.3.16 TO DISPLAY IMAGE GIVEN ON MATRIX FORM WHICH DOES

NOT REQUIRE ANY TOOLBOX:

Operation: Matlab command: Displays an image which is

represented as the matrix X. imagesc(X)

Adjusts the brightness and s is a

parameter such that -1<s<0 gives a

darker image and , 0<s<1 gives

brighter image.

brighten(s)

Changes the colors to gray. colormap(gray)

If in case our image is not displayed in gray scale even after converting it

into the gray scale image. we then use the command colormap(gray) inorder

to "force" Matlab to use a gray scale when it is displaying an image.

Command imshow can be used If we are using Matlab with an Image

processing tool box installed



2.3.17 TO DISPLAY AN IMAGE GIVEN IN MATRIX FORM USING THE

IMAGE PROCESSING TOOLBOX:

OPERATION MATLAB COMMAND:To Display an image which is

represented as matrix X. imshow(X)

Zoom in by using the left and right

mouse button. zoom on

Turn off the zoom function. zoom off



CHAPTER-3

VIDEO CONTENT ANALYIS

3.1 VIDEO CONTENT ANALYSIS:

Numbers of terms are used in different industries and markets to describe

Video Content Analysis:

Analytics

Behaviour Recognition

Content Analysis

Concept Coding

Intelligent Video

Object Tracking

Smart CCTV

However they all describe the real time use of computer vision in a security

environment to monitor the CCTV camera feeds and assist the guard in his

or her decision making process.

Real-Time content-based access to live video data requires content analysis

applications that are able to process video streams in real-time and with an

acceptable error rate.

3.1.1. CONTENT-BASED VIDEO RETRIEVAL:

Video indexing should be analogous to text document indexing

To facilitate fast and accurate content access to video data, we should

segment a video document into shots and scenes



We should extract keyframes or key sequences as index entries for

scenes or stories.

Four processes involved by video-content analysis and indexing

Feature extraction

Structure analysis

Abstraction

Indexing

3.1.2 APPLICATIONS OF VIDEO-CONTENT ANALYSIS:

We can broadly classify users into two extremes:

Nontechnical consumer

Trained, technical, professional corporate users who regularly

use the products

Professional and educational applications

Consumer domain application

3.1.3 PROFFESIONAL AND EDUCATIONAL APPLICATIONS:

Automated authoring of Web content

Searching and browsing large video archives

Easy access to educational material

Indexing and archiving multimedia presentation

3.1.4 CONSUMER DOMAIN APPLICATIONS:

The widest audience for video-content analysis is consumers

Differences between large archives and consumer domain

Video overview and access



Video content filtering

Video Content Analysis is the name commonly given to the automatic

analysis of CCTV images to create meaningful information about the content.

The scope of VCA is certainly impressive, and expanding all the time, for

instance it can now be applied for external and internal intruder detection; the

monitoring of plant or buildings for health and safety; people counting;

automatic traffic event and incident detection; safety enhancements for public

areas; smoke and fire detection and camera failure or sabotage detection. In

theory any 'behaviour' that can be seen and accurately defined on a video

image can be automatically identified and an alert raised.

There is little doubt, when specified and installed correctly, that VCA can

have a positive impact on the effectiveness and return on investment of

CCTV systems by adding enhanced or increased capabilities to detect and

analyse post-event video.

Video-content analysis and indexing involves four process primarily

according to many researchers: feature extraction, structure analysis,

abstraction and indexing.

3.1.5 FEATURE EXTRACTION FOR CONTENT ANALYSIS:

Feature extraction is a critical process in content based video indexing. The

indexing schemes effectiveness depends upon the effectiveness of attributes

in the content representation. And easily extractable video features such as

color, shape, structure, layout, texture and the motion cannot be mapped

easily as semantic concepts like indoor and outdoor, the people, or scenes of

car-racing. Whereas In audio domain, features like the pitch, energy, and

bandwidth enables the audio segmentation and also classification. In a video

program visual content is a major source of information in the video-content

analysis the effective strategy is to use attributes which are extractable from

multimedia sources. Much valuable information is carried in other media

components like the text which is superimposed on the images, or it is



included as closed captions, the audio, and speech which accompanies the

pictorial component. For consumer and professional applications combined

and also cooperative analysis of components will be far more effective in

characterizing the video program. Examples for this type of approach are

the Informedia system, 1 AT&T’s Pictorial Transcripts system,2–5 and Video

Scout6.

3.1.6 STRUCTURE ANALYSIS:

This process structure analysis allows us for organizing according to their

temporal of the Video data structures and relations and thus it builds table of

contents. the next step in the overall video-content analysis is the video

structure parsing and it is the process of extracting temporal structural

information of video sequences or programs.

It also involves detecting the temporal boundaries and identifying of

meaningful segments of video. There are many effective and robust

algorithms developed for video parsing 7–11 for segmenting the video

program into its temporal composition bricks. these composition bricks would

be categorized in a hierarchy which is similar to the film storyboards ideally.

Top level always consists of sequences or stories, composed of sets of

scenes where the Scenes are further partitioned into shots. Each shot also

contains a sequence of frames recorded continuously and they represent a

continuous action in the time or space. we can automatically build a table of

contents for video program’s by using such structural information.

The most important step for the process of video structure parsing will be

segmenting of the video into individual scenes. A scene generally consists of

series of shots which are consecutive and grouped together as from

narrative point of view because they are shot in the same location or also

may be because they shares some of thematic content. This process of

detecting the video scenes is analogous to paragraphing in the text

document parsing, but it also requires a higher level of content analysis. two

approaches for automatically recognizing program sequences are: one is

based on film production rules, and the other is based on model of priori



program. But both of these have had a limited success because the only

logical layers of the representation is based on subjective semantics in the

scenes or stories in video and there is no universal definition and also rigid

structure exists for scenes and stories, shots are actual physical basic layers

in the contarst, those whose boundaries can be determined by the editing

points or where a camera switches on and off. shots are a very good choice

as a basic unit for video-content indexing, and also they provide basis for the

construction of a video table of contents fortunately as an analogous to

sentences or words in text document. The Shot boundary detection

algorithms that which rely only on visual information contained in the video

frames can also segment video into the frames with a visual contents

similarly.

It is not possible to grouping of the shots into semantically meaningful

segments like stories without incorporating the information from other

components of video program. Algorithms of multimodal processing which

involves processing of not only the video frames, but also the text, audio, and

speech components that which accompany them have been proven effective

in achieving this goal.

3.2 PEOPLE COUNTING/ FOOTFALL:

Accurate people counting gives us powerful intelligence for strategic

planning. People counting technology has transformed the way how business

decisions are made. It is practical way to get clear picture about the picture of

occupancy, pedestrian flow and retail traffic. It also makes customer

behavoiur transparent which reveals how visitors respond in their movements

around a site.

With help of People counting systems we can…

Immediately evaluate and adapt marketing activities according to

retail traffic.

Improve customer service and reduce staff costs by matching staff

levels to varying occupancy.



Make best use of low occupancy periods, for example with

maintenance activities.

Assess sales conversion rates and see how new product lines or

services affect footfall.

Identify high performing stores and pinpoint the reasons for their

success

A footfall dashboard puts you in control of the data…

• See total and average footfall to date and for any given period.

• Compare different periods over a number of years with graphs and

statistics.

• Automatically highlight key dates such as bank holidays.

• Create a journal to evaluate and compare your own special events

and marketing activities.

• Upload sales data to see sales conversion rates.

• Analyse data against number of staff working.

• View and compare parameters such as occupancy, footfall or dwell

time.

3.3 FENCE PROTECTION (INTRUDER DETECTION)

Installation of security devices for purpose of detecting entry into a

designated security area is called fence protection. Intruders can be detected

and located anywhere along the fencing virtual zone by cctv cameras.

3.3.1 FENCE PROTECTION TECHNIQUES:

Dettering of thresspassing (9000 volt- safe shock wire)

Detection of such an attempt –warning security personnel

Scaring away threspassers (very loud siren and flood light)



Fig 3.1: Fence showing a virtual zone in red



CHAPTER-4

ALGORITHMS

4.1 THRESHOLDING:

In many vision applications, it is useful to be able to separate out the regions

of the image corresponding to objects in which we are interested, from the

regions of the image that correspond to background. Thresholding often

provides an easy and convenient way to perform this segmentation on the

basis of the different intensities or colors in the foreground and background

regions of an image.

In addition, it is often useful to be able to see what areas of an image consist

of pixels whose values lie within a specified range, or band of intensities or

colors. Thresholding can be used for this as well.

Fig 4.1 Threshold, Density slicing

Thresholding is method used in image processing to convert gray scale

image to binary image. In this process individual pictures are marked as

object pixels if their value is greater than some threshold value. And they are

otherwise called as background pixels. The segmentation is determined by a

parameter known as intensity threshold. Each pixel in image is compared

with this threshold and if pixels intensity is higher than the threshold then

pixel is set to white in output. If it is less than the threshold then it is set to



black. Multiple thresholds can be set in more sophisticated implementations.

So as a result band of intensity values can be set to white while everything

else is set to black. The object pixel value is 1 while background pixel value

is 0 and as a result binary image is obtained. The object must be brighter

than background. Thresholding is a non-linear operation which converts a

gray scale image into a binary image. In this process two levels are assigned

to the pixels that are above or below a specified value of threshold.

Threshold can be applied directly to command line

Syntax:

myBinaryImage = myGrayImage > thresholdValue ? 255 : 0

The key parameter in this process is selection of threshold value. There are

many methods in choosing mean and median value. The main thing which

should be considered is object pixels must be brighter than background.

It is more efficient if we use image threshold operation which provides

various methods for finding the optimal threshold value for a image. The

following methods are used to determine threshold value.

Automatically calculating a threshold value by using iterative

method

The histogram of the image must be approximated as bimodal

distribution and a midpoint value must be choosen as threshold

level.

Adaptive thresholding is a method used for evaluating the

threshold based on last 8 pixels in each row by using alternating

rows. This method is not supported when used as a part of

operation image edge detection.

Fuzzy thresolding by using entropy as a measure of fuzziness.

Fuzzy thresholding by using a method which minimizes a fuzziness

measure by involving the mean gray level in the object and

background



Default method where we must use the/T flag to specify a

threshold value.

4.2 ADAPTIVE THRESHOLDING

Fig 4.2 Adaptive thresholding or Dynamic thresholding

Thresholding can be used for segmenting an image by setting all the pixels

whose intensity value is above a threshold into a foreground value and all the

remaining pixels into background value.

Conventional thresholding operator generally uses a global thresholding for

all pixels, where as adaptive thresholding will be changing the threshold

value dynamically over the image. This is most sophisticated version of

thresholding and it can accommodate changing lighting conditions in the

mage which those occurrs as a result of a shadow or strong illumination

gradient .

Histogram of Adaptive thresholding takes input as a gray scale or a color and

outputs a binary image using simplest implemenatation which represents the

segmentation. In an image threshold has to be calculated for each pixel in an

image and If the pixel value is below the threshold will be set to the

background value, or otherwise it will assume the foreground value.

Two main approaches for finding the threshold are: (i) the Chow and Kaneko

approach and (ii) local thresholding. For both methods the assumption is

that smaller image regions will be more likely to have uniform illumination



approximately, and hence it is more suitable for thresholding. The Chow and

Kaneko divides an image into an array of overlapping subimages and then

they find optimum threshold for the each subimage by process of

investigating its histogram. By interpolating the results of the subimages

threshold for each single pixel can be found. The main drawbacks of this

method is that it is computational expensive and as a result this is not

appropriate for the real-time applications.

Another approach for finding the local threshold is to examine statistically the

values of intensity for the local neighborhood in each pixel. The which is

most appropriate statistic depends on the input image largely. The Simple

and fast functions will include the mean of local intensity distribution,

The median value,

The mean of the minimum and maximum values,

The neighbourhood size has to be large enough for covering sufficient

foreground and background pixels or otherwise a poor threshold value is

chosen. choosing regions which are too large can also violate the

assumption of approximately uniform illumination on the other hand. This

method is computationally less intensive than Chow and Kaneko approach

and will produces good results for many applications.

Adaptive thresholding can be used for separating desirable foreground image

objects from the background based on the difference in pixel intensities of

each region like global thresholding, Global thresholding will use a fixed

threshold for all the pixels in an image and therefore it works only when the

intensity histogram of a input image contains a neatly separated peaks which

corresponds to the desired subject and background. And as a result it cannot

deal with images containing a strong illumination gradient.



Whereas the local adaptive thresholding will select an individual threshold for

each pixel depending on the range of intensity values in local neighbourhood

allowing the thresholding of an image whose global intensity histogram don’t

contain any distinctive peaks.

4.3 HISTOGRAM:

The histogram of a image generally refers to histogram of pixel intensity

values. It is a graph which shows number of pixels in an image at each

different intensity value found in that image. For an 8 bit gray scale image

there are 256 different possible intensities and thus a histogram displays

graphically 256 numbers showing the distribution of pixels among those

grayscale values.

Not all images can be segmented neatly into foreground and background

just by using simple thesholding. intensity histogram of an image determines

wether the image can be correctly segemented.

If there is any possibility for separating the foreground of an image based on

the basis of pixel intensity, then the intensity of pixels within the foreground

objects must be different distinctively from intensity of pixels within the

background. And In such case, we will expect to see a distinct peak in the

histogram which will correspond to the foreground objects such that values

of thresholds can be chosen inorder to isolate this peak accordingly. it would

be unlikely that simple thresholding will be producing a good segmentation if

such a peak does not exist. adaptive thresolding will be better in such a case.

Here are some typical histograms along with some suitable choices of

threshold values.



Figure 4.3 A) Shows a classic bi-modal intensity distribution. This image can be

segmented successfully by using a single threshold T1.

B) Is slightly more complicated one and here we will suppose that central peak will

represent the objects we are interested in and hence the threshold segmentation will

require two thresholds: T1 and T2.

In C), The two peaks of the bi-modal distribution will run together and so it is almost

not possible to successfully segment this image by using a single global threshold

Thresholding can be often useful in many applications like remote sensing

where it will be desirable to select out those regions whose pixels lie within a

specified range of values of pixel within a image.



Fig 4.4 Original image / Gray scale image

Fig 4.5 Binary image



If we Imagine a poker playing robot that needs to visually interpret the cards

in its hand

Fig 4.6 Original image

Fig 4.7 Thresholded image



If you choose the threshold wrong the results can be disastrous

Fig 4.8 Threshold too low

Fig 4.9 Threshold too high



Fig 4.10 Effect of uneven illumination in single value thresholding

Uneven illumination can really upset a single valued thresholding scheme



These images show the troublesome parts of the previous problem further

subdivided

An approach to handling situations in which single value thresholding will not

work is to divide an image into sub images and threshold these individually

Since the threshold for each pixel depends on its location within an image

this technique is said to adaptive

The image below shows an example of using adaptive thresholding with the

image shown previously

Fig 4.11Adaptive thresholding

As can be seen success is mixed

But, we can further subdivide the troublesome sub images for more success .

4.4 CONNECTED COMPONENTS LABELING:

Based on connectivity of the image the connected components labeling will

scan an image and will group its pixels based on image connectivity into

components. All pixels in each connected component will share a similar

pixel intensity values and they will be connected to each other in some way.

Once after all groups are determined then the each pixel is labelled using a

graylevel or a color according to the component it was assigned to. This

process of extracting and labeling various disjoint and connected

components in an image is central to many image analysis applications.



The connected components labeling will work by scanning an image pixel-by-

pixel from left to right and as well as top to bottom to identify connected pixel

regions. That are regions of adjacent pixels which will share the same set of

intensity values V. for binary image v={1} .incase of a graylevel image V will

take range of values like V={51,52,53,….,77,78,79,80}.

The Connected components labeling works on both binary and gray level

images and there are possible different measures of connectivity. if we

assume binary input images and 8-connectivity. The connected components

labeling operator will scan the image by moving along a row until it comes to

a point p in which p represents pixel to be labeled at any stage in the

scanning process and for which V={1}. Incase if this is true, it will examine

the four neighbors of p which already have been encountered in the scan

process. They are the neighbors (i) on the left of p, (ii) above to it, and (iii )

and (iv) which are the two upper diagonal terms. Dependingupon information

given, the labeling of p will occur as below

If all the four neighbors are 0, then we assign a new label to p, else

if only one of neighbor has V={1}, we will assign its label to p, else

if more than one of the neighbors has V={1}, we assign one of the

labels to p and will make a note about the equivalences.

Once after completion the scaning process, label pairs which are the

equivalent will be sorted into equivalence classes and a unique label will be

assigned for each class. Finally, a second scan should be made through an

image, and during which each label will be replaced by the label assigned to

equivalence classes. the labels might be different graylevels or colors in the

display.

For extracting connected components and to label them in various ways a

collection of morphological operators will be existing. in a simple method for

extracting te connected components of an image will combine operations of

dilation and mathematical intersection. The former one will identify the pixels

which are the part of continuous region by sharing a common set of intensity

values V={1} and the latter will eliminate dilations which are centered on



pixels with V={0}. The structuring element which is used will define the

desired connectivity.

More sophisticated variants of this will include a set of functions known as

geodesic functions used for measuring the exact shape of distinct objects in

a image. All these operators are based upon the notion of the geodesic

distance d which is defined as the shortest distance between two points

which are located within an image object such that the entire path between

the points is included in the object. One way to obtain this is to apply a series

of dilations of size 1. For example, we consider the image

Fig 4.12: Original image

Which shows a triangular block. By applying a geodesic operator to the

image it produces a labeled image

Fig 4.13: Result of applying geodesic operator

Where as graylevel intensity labeling which is across the surface of the block

will encode geodesic distance. As a result . light pixels represent larger

distances.

Connected Component Labeling is commonly used to refer to the task of

grouping the connected pixels in an image. (The basic approach is to scan

the image and assign labels to each pixel until the labels for the pixels no

longer change. This basic approach is slow because the labels propagates

one layer in an iteration. There are two common strategies to speed up this

process. The most common strategy leads to a two-pass algorithm. This

algorithm uses a data structure to record label equivalence information. It

scans the image once to assign provisional labels and discover the label



equivalence information, and scans the image a second time to assign the

final labels. Another very successful strategy is the one-pass algorithms, that

find all connected pixel in one shot, for example through recursively visit all

the connected neighbours. One of the most successful approach in this

category is the Contour Tracing algorithm by Chang et al. They also distribute

an implementation of their algorithm online. The most commonly used data

structure for recording the label equivalence information in a two-pass

algorithm is the union-find data structure. Researchers have recognized the

possiblity of implementing this data structure implicitly using an array instead

of pointers. We designed a set of algorithms that enabled us to operate on

this array efficiently, and prove that a two-pass labeling algorithm with this

implicit union-find data structure is theoretically as good as the best known

labelling algorithms. We further show through experimental measurements

that it in fact is faster than the best known one-pass algorithms.

Fig 4.14: Original image

Fig 4.15: Thresholded image



Fig 4.16: Labels coded as gray values

Fig 4.17: Labels coded as colours

Fig 4.18: Labels coded as 8 different colours

4.5 MEDIAN FILTERING:

Smoothing low pass filters reduce noise but however the underlying

assumption is that neighboring pixels represent additional samples of the

same value as reference pixel, i.e. they represent the same feature. But at

the edges blurring of features results. This process is a linear process using

convolution techniques to implement weighting kernels as neighborhood

function. There are also nonlinear neighborhood operations which can be

performed for the purpose of noise reduction which can do a better job of



preserving edges than simple smoothing filters. That method is called as

median filtering. In median filtering the neighboring pixels are ranked

according to brightness i.e. intensity and the median value becomes new

value for central pixel. median filters can do an excellent job of rejecting

certain types of noise, in particular, “shot” or impulse noise in which some

individual pixels have extreme values. In median filtering operartion, the pixel

values in the neighbourhood window are ranked according to intensity, and

the middle value(median) becomes the output value for the pixel under

evaluation.

In particular, compared to the smoothing filters examined thus far, median

filters offer three advantages:

No reduction in contrast across steps, since output values available

consist only of those present in the neighbourhood (no averages).

Median filtering does not shift boundaries, as can happen with

conventional smoothing filters (a contrast dependent problem).

Since the median is less sensitive than the mean to extreme values

(outliers), those extreme values are more effectively removed.

The median is, in a sense, a more robust “average” than the mean, as it is

not affected by outliers (extreme values). • Since the output pixel value is one

of the neighbouring values, new “unrealistic” values are not created near

edges. • Since edges are minimally degraded, median filters can be applied

repeatedly, if necessary.

Considerations:

The median filter is more expensive to compute than a smoothing

filter. Clever algorithms can save time by making use of repeating

values as the neighbourhood window is slid across the image.

Median filters are nonlinear:

Median[A(x)+B(x)] median[A(x)]+median[B(x)]�

This must be taken into account if you plan on summing filtered images.

Median Filtering

The median for each subgroup is determined.



These two values are then compared to the original pixel value, and

the median for these three values becomes the output value for the

pixel in the filtered image.

Larger neighborhoods permit the defining of additional subgroup

orientations.

4.6 HYBRID MEDIAN FILTERING:

Median filters can tend to erase lines narrower than ½ the width of the

neighbourhood. They can also round off corners.

Hybrid median filters can get around these problems.

The hybrid median filter is a three step ranking process that uses two

subgroups of a 5x5 neighbourhood.

These subgroups are drawn from pixels parallel to the image frame

edges, and at 45º to the edges, centred on the reference pixel.



4.7 FLOWCHART SHOWING PROCESS HOW THIS PROJECT

WORKS GENERALLY:

threshold too low threshold too high

If there is any noise

Fig 4.19: Showing actual process of this project


Cctv starts recording

Reads the video in mat lab

Select threshold

value

Thresholded image

Median filterlabeling

If intruder is detected entering fence alarm starts

When intruder approaches fence virtual zone appears red

and alerts


A fence is monitored continuously by CCTV cameras. I converted the images

recorded by CCTV into gray scale and then converted to binary. i.e., the

images are thresholded to obtain binary images. If the selected threshold

value is too low or too high change the threshold values. Then median filter is

used to remove any noise. The value of threshold must be adjusted such that

region of interest is separated from background. Then labelling is done to

identify the object and based on based on pixel connectivity. Once the CCTV

detects that intruder has approached virtual zone it alerts security persons.

When intruder actually enters virtual zone and tries crossing fence alarm

starts.



CHAPTER-5

RESULTS

The results of the proposed algorithm which was explained in the previous

chapter are illustrated and demonstrated here. The proposed algorithm was

applied to protect a fence in my local area. A virtual zone was created to

detect any intruder enters the fence area. This virtual zone appears green if

no body enters it, however it immediately goes red when that zone is

breached.

The following figures demonstrate how the algorithm is successfully capable

of protecting the fence. The demonstration of the proposed algorithm is

carried out by showing different frames and zone status while an intruder was

approaching the zone as will be seen in the figures below.

(a) (b)

Fig 5.1. (a) Green virtual zone as an intruder approaching the zone, (b) Foreground

objects in the scene.

The fig 5.1 shows an fencing and an intruder moving and the virtual zone is

green because he is away from virtual zone. (a) shows original image and

(b)shows thresholded image.



(a (b)



Fig 5.2 shows virtual zone in green because intruder has not approached the

virtual zone.( a) shows original image and (b) shows thresholded image.

(a) (b)



Fig 5.3 still shows virtual zone in green because intruder has not come into

fencing virtual zone. (a) shows original image and (b) shows thresholded

image.



(a) (b)



Fig 5.4 also shows virtual zone in green as intruder is not in virtual zone of

fencing. (a) shows original image and (b) shows thresholded image.

(a) (b)

Fig 5.5. (a) Red virtual zone as an intruder approaching the zone, (b) Foreground


Fig 5.5 shows virtual zone in red because the intruder has entered the virtual

zone and alerts security personnel. (a) shows original image and (b) shows

thresholded image.



(a) (b)

Fig 5.6. (a) Red virtual zone as an intruder approaching the zone, (b) Foreground


Fig 5.6 shows that intruder is trying to climb the fence. The virtual zone is red

and alarm starts ringing. (a) shows original image and (b) shows thresholded

image.

As shown in the previous figures, the algorithm was successful capable of

protecting that fence in the last particular example, However there are yet

many challenges and problems may face this algorithm, such as object's

shadow, light variations, camera displacement, … etc. all of these parameter

may lead to wrong conclusion and lead to false Alarm. Fig 5.7 illustrate the

effect of show in the proposed algorithm, as shown in the figure, a false

alarm was set off when the shadow of the man enters the zone.



Fig 5.7: The effect of shadow in the proposed algorithm.

In fig 5.7 effect of shadow can be clearly observed. (a),(b),(c) shows virtual

zone in green because the intruder or his shadow did not enter the fencing

virtual zone whereas in (d) virtual zone is shown in red even before intruder

entering the virtual zone. It is because his shadow entered the virtual zone.

This may lead to condition such that alarm starts even if intruder don’t try to

enter the fence only because his shadow enters and his shadow is mistaken

as a object.


(a) (b)

(c) (d)


CHAPTER-6

CONCLUSION AND FUTURE

WORK

6.1 CONCLUSION:

In this project, I propose and implement a fence protection algorithm to be

used for the CCTV cameras. The project fence protection using CCTV

camera is developed successfully. When a intruder tried to cross the fence

CCTV has monitored and alarm has started. CCTV camera detected the

person immediately when he entered the virtual zone and prevented the

person entering fence by alerting security persons by giving an alarm sound.

I have taken the video and to separate the object of interest from background

I have done thresholding. I have adjusted threshold value such that object is

clearly visible. To remove any noise I have used median filter. I have done

labelling in order to track the object and check if it is in virtual zone.

As shown in the results, the proposed algorithm was successfully applied to

protect a fence against any intruder trying to approach that fence. In this

stage of the project, the current algorithm need to be adapted to deal with

more challenges such as light variations, shadows and many others.

this project is very useful in many high security areas where it cannot be

done manually. A person cannot be able to look through all the videos

recorded by cameras at the same time. Hence this project is developed such

that if the alarm starts every guard can be alert and prevent an intruder to

enter. It is mainly used in secured places like airports, banks, etc to prevent

any one entering restricted area which may cause loss of money and is

dangerous for people if some one enters an airport. This is a basic design



work and more performance will be gained when system is practically

employed.

6.2 FUTURE WORK:

Thus in the proposed system I introduced efficient way for security of fencing

using image processing based tracking algorithms, now for the future work

my main concern is to design practically the proposed system. Following are

main parts of future work:

Design of Target System: Using the algorithms related to the

image processing and intruder tracking target system will be designed.

Additional designs of target system: New mechanisms related

to the target system designs are proposed and will be implemented

additionally.

Improving the algorithm to be capable of detecting shadows,

light variations, background changes and other parameters.



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