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Transcript of Security System Using Motion Sensing Pptslides
8/4/2019 Security System Using Motion Sensing Pptslides
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Guided byProf. S.R.Kinge
Project by1.Omprakash Rai [B-3383067]2.Refad Shivani [B-3383096]3.Varun Agarwal [B-3383103]
SECURITY SYSTEM USING MOTIONSENSING
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Introduction
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ØThe moment we step out of our shopwe have a fear that someone would be
planning to stole our precious saving
and investments.
ØWe wish to make a full proof securitysystem that will provide tension freeenvironment for the same.
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ØIn today's times the extent of robberiesof valuable equipment has gone up to a
great extent.
Ø Thus due to the failure of such securitysystems we studied about anddeveloped our own concept of securitysystem where in we try to cover allbases possible to safeguard the
equipment
MOTIVATION FOR THE
PROJECT
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Literature Survey
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WHAT IS
SECURITY ?
ØFORM OF PROTECTION
ØHOWEVER SECURITY SYSTEMSAVAILABLE TODAY INSUFFICIENT
ØTHUS WE PRESENT TO YOU OUR SECURITY SYSTEM
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TYPES OF SECURITY SYSTEMS
ØLOCK ØSAFETY DEPOSIT BOXØMETAL DETECTOR ØBIOMETRIC SECURITY SYSTEMØPERIMETER SECURITY SYSTEMØ
AUTODIALLER ALARMØMULTIMETER WAVE SCANNER
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TECHNOLOGY USED IN SECURITYSYSTEMS
Ø RADAR
ØMOTION SENSING
ØPASSIVE INFRARED SENSORS
ØULTRASONIC SENSOR
ØMICROWAVE SENSOR
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TYPES OF MOTION SENSORS
Ø INFRARED SENSOR
ØMICROWAVE SENSOR
ØDIGITAL CAMERA
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CAUSE
One picture is worth more
than thousand words
WHY USING DIGITAL
CAMERA?
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SOFTWARES AVAILABLE
Ø MATLAB
ØTURBO C
Ø
JAVA
ØLabVIEW
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WHY MATLAB?
Ø EASY TO UNDERSTAND
ØSIMPLE TO USE
ØEASILY AVAILABLE
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Block Diagram
BMPFILE
EXTRACTING FEATURESOF IMAGE
COMPARINGFEATURESOF IMAGE WITHDATABASE
FEATURESFROMDATABASE
INITIATE A CALLOR A ALARM
CAMERA
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COMPONENTS USED
Camera
ØIR CAMERAØ1.3 Megapixels
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MIDDLEWARE
(PC , LAPTOP)
WHY A PC AND WITH
WHAT SPECIFICATIONS?
ØEASILY AVAILABLEØPOWERFUL PROCESSOR Ø
EASY TO USEØEXTRA CAMERA-USB 2.0
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BUZZER/ALARM
ØBuzzer with high frequency output
GSM MODULE
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Workin
gØImage Acquisition
ØBMP file format
Ø various filters we intend to use
ØExtracting information fromhistogram
ØV ri l rithm t nh n
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A digital image is a representation of atwo-dimensional image as a finite set of digital values, called picture elements orpixels
Image
Acquisition
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1 pixelPixel values
typically representgray levels, colors,heights, opacitiesetc
In digitalization thereare approximation of a real image
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BMP fileformat
Image formats include:1 sample per point (B&W or Grayscale)3 samples per point (Red, Green, and Blue)4 samples per point (Red, Green, Blue, and “Alpha”,a.k.a. Opacity)
For this project we will focus on grey-scale images
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Ø Given an image,recognize(identify) objects
Ø The hard part: the same object can look
incredibly different in different images dueto differences in view points, illumination,color, occlusions,… etc.
Ø A robust recognizer must develop invariantrecognition (translation, size, rotation?)
NATURE OF THE PROBLEM
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Static
Camera
MobileCamera
Dt(x,y)=|I1(x,y)−I2(x,y)|
Cami
Cami
Dt(x,y)=|I1(x,y)
−I2(x,y)|
V
AdaptiveThreshold
(x,y) = 1if
Dt(x,y) >Th
=0otherwise
Tht.
ChangeDetection
Multi-CameraRegistration
Chang
eDetection
ChangeDetection
ImageAcquisition
ImageAcquisition
CamerasCalibration
Cam 1
Cam n
Ref. IEEE SIGNAL PROCESSING
M ti D t ti U i Diff ti l
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Motion Detection Using DifferentialHistogram Equalization
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ac groun e ec on on
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ac groun e ec on onbasis of two frames
Ø The algorithm of two-frame-based B/F detectionis described below.
Ø f I: A pixel in a current frame, where I is theframe index.
Ø f I-1 : A pixel in a previous frame ( f I and f I-1 atthe same location.)
d I=
|f I - f I-1|
Bi = 1if bi>th.
else= 0
if bi<th.
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q The OriginalImage
q
The result of difference of two
Ø d I: Absolute difference of f Iand f I-1 . d I= |f I - f I-1 |
Øb I : B/F mask - 0: background.0xff: foreground.
Ø T: Threshold value.
ØIf d I > T, f I belongs to theforeground; otherwise, itbelongs to the background.
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ØAfter Differential
HistogramEqualization
Ø The result of adaptivethresholding
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qParticleremoving
q Motion boundary
ØThe some simple and smallnoise which is remained can be
removed by morphological processing
Ø It is done by two stages of Morphology algorithm(Dilation/Erosion)
Ø uniform and binary region of the movingobject will be achieved byusing the filling algorithm
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q Firstmovingobject
q Secondmovingobject
ØIt is not necessary to transmit the entireimage.
ØWe just extract the
moving images from the boundary thatwas obtained.
ØFinally, extracted images from
moving object are sent through LANand/or Modem toCentral Security System.
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ADVANTAGES
ØAutomatic security system
ØEasily installableØgood efficiency for fast detectionØLow maintenance cost
ØEasy to operateØUser helpfullyØReduce burden of operator
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ØUsed in high security areas like
bordersØSecurity of a MansionØUsed in museumØ Used in mall and shops
Application
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Future Scope
ØWith the help of a stereo photogrammetriccamera it can be mounted on vehicles as a
combination of hardware and software toavoid accidents by motion sensingØIt can be used in automationØWe can be used GSM module in it.
ØIn high security areas we can use facerecognition.ØWe can used gesture recognition with it.
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Security of a Mansion
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Current Status Of The Project
Ø We are done with literature survey and synopsisØWe have understood the various requirements todevelop a full proof security areasØ
We have carried out an extensive literaturesurveyØWe have studying most of the algorithms.ØWe have studied books like gonzalez and sonkaØWe have also studies projected related IEEE papers
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REFERENCES BOOKS
“Digital Image Processing”, RafaelC.
Gonzalez & Richard E. Woods,
“Digital Image Processing”
Sanka
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REFERENCES
ØMotion Detection Using Differential HistogramEqualizatioR. Hasanzadeh P.R.*, A. Shahmirzaie**, A.H. Rezaie***Electrical Engineering Department, Amirkabir University of Technology, Hafez Avenue,15914, Iran.
0-7803-9314-7/05/©2005 IEEE
ØMotion Tracking with an Active CameraDon Murray and Anup Basu, Member, IEEE
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND
MACHINE INTELLIGENCE. VOL. 16, NO. 5, MAY 1994
ØActive Video-Based Surveillance SystemIEEE SIGNAL PROCESSING MAGAZINE [25] MARCH 2005Gian Luca Foresti, Christian Micheloni,
Lauro Snidaro, Paolo Remagnino, and Tim Ellis