Video Camera Security and Surveillance System

ICAMES 2008

Team Members : Semih Altınsoy Denis Kürov

Team Advisor: Assist. Prof. M. Elif Karslıgil

May, 2008

YILDIZ TECHNICAL UNIVERSITYCOMPUTER ENGINEERING DEPARTMENT

Introduction General System Modules Motion Detection Face Detection Face Recognition Zone Network Conclusions and future work

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 by face 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.

That system’s abilities are;

Burglar following

Prevent accessing forbidden areas

Child protection for accidents etc.

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 Compute Eigenspace Project the Training Data Identify the Test Images - Zone - Network

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.

This technique is a face detection in YCbCr color space.

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.

C’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.

And this is the ellipse formula for skin tones.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 SpaceReference: Face Detection in Color Images - Rein-Lien Hsuy, Student Member, IEEE, Mohamed Abdel-Mottaleb, Member, IEEE, Anil K. Jainy,

Fellow, IEEE

Find Eyes and Mouth. 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

Eigenface (Training Stage)

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

Eigenface (Testing Stage)

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!

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.

Networking part (server)

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 !

Networking Part (Client and Server Case Study)

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

In conclusion, this system can make burglar 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.

THANK YOU

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