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ICAMES 2008 Team Members : Semih Altınsoy Denis Kürov Team Advisor: Assist. Prof. M. Elif Karslıgil May, 2008 YILDIZ TECHNICAL UNIVERSITY COMPUTER ENGINEERING DEPARTMENT.

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Video camera security and surveillance system

ICAMES 2008

Team Members : Semih Altınsoy

Denis Kürov

Team Advisor: Assist. Prof. M. ElifKarslıgil

May, 2008

YILDIZ TECHNICAL UNIVERSITY

COMPUTER ENGINEERING DEPARTMENT

Video Camera Security and Surveillance System


Table of contents

Introduction

General System Modules

Motion Detection

Face Detection

Face Recognition

Zone

Network

Conclusions and future work

TABLE OF CONTENTS


Introduction

The system is a video surveillance security system which alerts users for some situations.

The main workflow is that a fixed security camera will capture the videos continuously to checkout if there is movement in the area.

If there is any movement in the zone, the movement differentiation between the current and previous one. If the diffrence is above the predefined value, the object will be checked if it is human or not.

If the moving object is a human, its identity will be compared with current image database byface recognition module. If the face identity is not found in the database, the user will be alerted about current situation.

We will have permission rules for detection also. User can select rules for sending alarms, allow people, allow motion etc.

Introduction


Introduction1

That system’s abilities are;

Burglar following

Prevent accessing forbidden areas

Child protection for accidents etc.

introduction


General system modules

System has five main module.

- Motion Detection

- Face Detection

Lighting Compensation

Skin Tone Detection in YCbCr Space

Find eyes and mouth

Resize Face

- Face Recognition

ComputeEigenspace

Project the Training Data

Identify the Test Images

- Zone

- Network

GENERAL SYSTEM MODULES



Motion detection

Motion detection is a trigger for face detection and recognition module.

For motion detection we are looking for current and previous frame.

(Current Frame – Previous Frame) > Threshold

For this situation, we can understand that there is a motion.

Threshold is a value for motion sensitivity.

Motion detection


Face detection

This technique is a face detection in YCbCr color space. recognition module.

Red-Green-Blue space is not a best choice for face detection.

Firstly system corrects the color with a lighting compensation technique. It uses reference white to normalize the color appearance.

The corrected RGB components are nonlinearly transformed in YCbCr color space.

Face Detection based on the cluster (Cb/Y)-(Cr/Y) subspace.

Skin-tone pixels are detected using an elliptical skin model in transformed space.

Face detection


Face detection1

C’ recognition module.i(Y)= { (Ci(Y) – Ci-(Y)) ∙ Wci / Wci(Y) + Ci-(Kh) if Y<Kl or Kh <Y

{ Ci(Y) if Y Є [Kl, Kh],

Wci(Y)= { WLci + ((Y-Ymin) ∙ (Wci - WLci))/ (Kl - Ymin) if Y< Kl

{ WHci + ((Ymax-Y) ∙ (Wci - WHci))/ (Ymax- Kh) if Kh<Y

Cb-(Y)= { 108 + ((Kl - Y) ∙ (118-108)) / (Kl - Ymin) if Y< Kl

{108 + ((Y- Klh) ∙ (118-108)) / (Ymax- Kh) if Kh<Y

Cr-(Y)= { 154 - ((Kl - Y) ∙ (154-144)) / (Kl - Ymin) if Y< Kl

{154 - ((Y- Klh) ∙ (154-132)) / (Ymax- Kh) if Kh<Y

We calculated transformed CbCr subspace with this transformation.

Face detection


Face detection2

And this is the ellipse formula for skin tones. recognition module.

x = (cosθ * C’b(Y)-cx) + (sinθ * C’r(Y)- cy)

y= (-sinθ * C’b(Y)-cx) + (cosθ * C’r(Y)- cy)

(x-ecx)2 / a2 + (y-ecy)2 / b2 = 1

Transformed CbCr Space

Reference: Face Detection in Color Images - Rein-Lien Hsuy, Student Member, IEEE, Mohamed Abdel-Mottaleb, Member, IEEE, Anil K. Jainy, Fellow, IEEE

Face detection


Face detection3

Find Eyes and Mouth. recognition module.

The color of mouth region contains red component and weaker blue component. So the chrominance component Cr is stronger than Cb in mouth region.

Mouth Map= Cr2 ∙ ( Cr2 – η ∙ Cr/Cb)2

η = 0.95 ∙ ((1/n) ∑Cr(x,y)2 ) / ( (1/n) ∑ Cr(x,y)/Cb(x,y) )

After the facial feature detection module rejects the regions that do not contain any facial features, we will find eyes of the face with black dots in the up-half of the picture.

Reference: Face Detection in Color Images - Rein-Lien Hsuy, Student Member, IEEE, Mohamed Abdel-Mottaleb, Member, IEEE, Anil K. Jainy, Fellow, IEEE

FACE DETECTION


Face recognition

Eigenface (Training Stage) recognition module.‏

Load The image data into memory

Produce a centered image from all

Create data matrix

Create the covariance matrix

Compute the eigenvalues and eigenspaces

Order The EigenVectors

Each Trainig data is projected into eigenspace

Every sub eigenspace stored in XML for future use

Face Recognition


Face recognition1

Eigenface (Testing Stage) recognition module.‏

Pull previous XML data into memory

Test image is mean centred

Mean centred image is projected into eigenspace

The sub eigenspace is compared with all training ones

If a very close match occurs the person is identified

Otherwise you are a bad person,sorry!

Face Recognition


That module is responsible for the rules that will be applied on different places on the screen.

When a new frame is taken, firstly it should pass the Zone Module's rules.

-add_Policy : That sub module adds a new policy to the current screen section. The options are “allow_all”,”deny_all”,”allow_only [list of users]”,”deny_only [list of users]”. The policies are kept in linked list object.

-remove_Policy: That one removes a policy rule.

-edit_Policy: Edits the mentioned one.

-list_Policy: Gets all list that was added.

-apply_policy : It works as follow; it scans the current security list and if it gets a match, it quits. If the apply_policy gets a negative result, it is an alert.

Zone


Network

Networking part (server) applied on different places on the screen.

Collecting alert videos and images

Responsible for Authentication and Authorization

Provides safer Data transfer for clients

Sends the videos and images to mobile and other clients

Uses X.503 certificates and SSL connections

Supplies log search and management for client parts

No malicious users around !

Network


Network1

Networking Part (Client and Server Case Study) applied on different places on the screen. ‏

At the beginning Server produces its certificate(root)‏

Minions (clients) make server sign their certificates

They are known clients and can send data to server

A stranger is inside and detection part send alert with video

Client passes the security of server and send its video

Server now can alert the other minions that it signed before

Everything is safer when it is crypted with 256 bit

NETWORK


Conclusions and future work

In conclusion, this system can make b applied on different places on the screen. urglar following, prevent accessing forbidden areas, child protection for accidents etc.

This system is a real time system. So its some features like face recognition and detection can be better and more faster.

Also for the future work there can be a mobile part.

The system can be manageable by remote clients like mobile phones, PDAs and other PCs. In that way users can receive and control alerts of the system and manage logs, videos that system saved as critical.

User can connect remote desktop computer with his/her mobile phone and control, manage logs and videos that system saved.

Conclusions and future work


THANK YOU applied on different places on the screen.

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