VRS Final [Compatibility Mode]

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External Presentation of BS CS

Final ProjectDate: 15th June 2010Session (2006-2010)

Title: Video Reporting System

Lahore College for Women University, Lahore

Presented by: Saira Qamar (671)Sumaira Rahman 678

Rabia Javeed (681)


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Video Reporting SystemExternal Presentation of BS(CS) Final Project

Outline of the Presentation

Introduction

SDLC Phases

Inception a ora on Construction Transition

Conclusion and Future Enhancements References

Presented By: Sumaira Rahman (678)


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Introduction

Video Reporting System

Videos are

taken fromAfterprocessing a Its a desktopbased

the CCTVcamera forfurther

rocessin

report is

generated forthe top

applicationcontain one

package.

the ro ect !!for necessaryactions



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Introduction

SDLC Phases





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It is any form of signal processing for which the input is an

image, such as photographs or frames of video; the output of

Start

Literature Review Purpose Implementation Reduce Theft

image processing can be either an image or a set ofcharacteristics or parameters related to the image

Databases consist of software-based "containers"

that are structured to collect and store information sousers can retrieve, add, update or remove such

Object Detection

Introduction about Surveillance S stem

omponen s Enhance Productivity Provide Internet Viewable Monitor Multiple Locations

Hardware Aspects CCTV cameras Sensors

Image processing

Study multiple TrackingMovie storage

inStop

Advantages of Surveillance System

Software Aspects

Algorithms Databases

For contain the reportsof the s stem

,algorithms and

tools Activities

analysis

Areas related to Project



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Introduction

SDLC Phases



Presented By: Rabia Javed (681)


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SDLC Phases

RUP (Rational Unified Process)



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Video Reporting SystemExternal Presentation of BS(CS) Final Project Project Model Project schedule and Mile stones Deliverables

Inception

SDLC PhasesThe Video reporting System is a desktop system in which CCTV camera is used

Requirement Change Management Plan Configuration Plan Quality Plan

RUP (Rational Unified Process) Phase I: Inception

Scope

for taking the suspicious activity of the human body in the form of reports for

necessary actions.

Resource Plan

Infrastructure Plan Risk identification Plan

ro em a emen System Architecture

Project Plan

The Video Reporting System is surveillance system that works for reporting the suspicious humanaction. The scope of the project is:

Requirements Functional

. Video is capture through CCTV camera. Human actions are recognizing in the live video. Report of human actions is generated for management. Our system cannot detect any abnormal action outside the cameras lenss boundary.

Hardware and Software Requirements Our system only deploy at main gate of any building for the purpose of security reason

containing barrier. Only Normal human body is detected e.g. children, animals detection is not part of our

project. . Humans which stay more then 5 seconds in the targeted boundary will be treated as

suspicious. Purchasing, installation and deployment of any Hardware, Software & caballing installation,

is not part of our project.


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SDLC Phases Hardware Requirements

CCTV Camera or Webcam Intel Processor 2.0GHz

RUP (Rational Unified Process) Phase I: Inception

Scope Maintainability

Software Requirements

Documentation: Microsoft Office

Object Detection

ro em a emen System Architecture

Project Plan

Usability

Efficiency

Rational roseVisio

Coding: C #.net frame work

uman o y rac ng

Reporting

Requirements- Functional-

Operating System: Windows XP

Hardware and Software Requirements



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SDLC PhasesRUP (Rational Unified Process) Phase II: Elaboration

Use case Diagram

Scenario I: Object Detection

Scenario II: Tracking

ass agram Sequence Diagram

Scenario III: Reporting

Presented By: Saira Qamar (671)


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Sequence Diagram

Scenario I: Object Detection



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Sequence Diagram

Scenario II: Tracking



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Sequence Diagram

Scenario III: Reporting



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User Interfaces



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SDLC Phases

RUP (Rational Unified Process) Phase II: Elaboration

User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces



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SDLC Phases


User Interfaces

Presented By: Saira Qamar (671)Presented By: Saira Qamar (671)


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User Interfaces

Presented By: Saira Qamar (671)Presented By: Saira Qamar (671)


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SDLC Phases

RUP (Rational Unified Process) Phase III: Construction

Project Demo:

Test Cases:

Object Detection Test Case

Human Body Tracking Test Case

Reporting Test Case

BS CS


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SDLC Phases

Deployment Diagram

RUP (Rational Unified Process) Phase IV: Transition

E t l P t ti f BS(CS) Fi l P j t


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Introduction

SDLC Phases




Vid R ti S tExternal Presentation of BS(CS) Final Project


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Conclusion

ur pro ec s a ou e repor ng o susp c ous umanactions. This system facilitates management as no

particular person is required to monitor human actionsat the place where surveillance is required. Thesystem reports automatically to management about




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Future Enhancements

Our system achieves human activity but still requiressome enhancements e. .

To make tracking of people through multiplecameras.

To enhance the tracking algorithm so that it did notslow down the system processing

identify and recognize human body by itself. To incorporate more actions like bending, jumping

.




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Introduction

SDLC Phases


Current Status Conclusion and Future Enhancements




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References

Saad Ali, Arslan Basharat and Mubarak Shah ChaoticInvariants for Human Action Reco nition, 2007.

N. Dalal and B. Triggs. Histogram of oriented gradients forhuman detection. In CVPR, 2005.

A. Farhadi and M. K. Tabrizi. Learning to recognize activitiesfrom the wrong view point. In ECCV, 2008.

M. Valera and S.A. Velastin, Intelligent distributed surveillancesystems: a review, IEE Proc.-Vis. Image Signal Process., Vol.

, . ,

Neeti A. Ogale, A survey of techniques for human detectionfrom video, 2006




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Video Reporting System( ) j

References

ArnoldWiliem, Vamsi Madasu, Wageeh Boles, and Prasad, ,

Actions, in Computer Vision and Pattern Recognition

Weilong Yang, Yang Wang, and Greg Mori, 2008, Human

Vision and Pattern Recognition

Wei Niu, Jiao Long, Dan Han, and Yuan-Fang Wang, 2004,Human Activit Detection and Reco nition for Video

Surveillance, in Computer Vision and Pattern Recognition Neeti A. Ogale, A survey of techniques for human detection




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Video Reporting System( ) j

References

P. Peixoto, J. Goncalves, and H. Araujo. Real-time gesturereco nition s stem based on contour si natures, ICPR, volume 1,pages 447-450, 2002.

A. Fathi and G. Mori. Action recognition by learning mid-level motion

features. In CVPR, 2008. D.Weinland and E. Boyer. Action recognition using exemplar-based

embedding. In CVPR, 2008.

Henry Schneiderman and Takeo Kanade. Object detection using the. , . , .

G. Gordon, T. Darrell, M. Harville, and J. Woodfill, Background

Estimation and Removal based on Range and Color, in Proceedingsof IEEE Conference on Com uter Vision and Pattern Reco nitionVol.2, pp.2459-2464, Fort Collins, CO., USA, Jun. 1999.




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p g y

References

Qiang Zhu, Shai Avidan, Mei-Chen Yeh, and Kwang-Ting Cheng,Fast Human Detection Usin a Cascade of Histo rams of OrientedGradients, 2005.

H. Zhong, J. Shi, and M. Visontai, Detecting Unusual Activity in

Video, Proc. IEEE Conf. CVPR, vol. 2, pp. 819-826, 2004. F. Lv, J. Kang, R. Nevatia, I. Cohen and G. Medioni, Automatic

Tracking and Labeling of Human Activities in a Video Sequence, IntlWorkshop on Performance Evaluation of Tracking and Surveillance,

.

P.C. Ribeiro and J. Santos-Victor, Human Activity Recognition fromVideo: modeling, feature selection and classification architecture, IntlWorkshop on Human Activity Recognition and Modeling, pp. 6170,2005.




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References

Anant Madabhushi and J. K. Aggarwal , 1999, A Bayesian ,

Image Understanding.

Aaron F. Bobick, James W. Davis, The Recognition of Human

Pattern Analysis and Machine Intelligence, Vol. 23, No. 3,March 2001.

Somboon Hon en Ram Nevatia Francois Bremond Video-

based event recognition: activity representation andprobabilistic recognition methods, Computer Vision and ImageUnderstanding96, (2004) 129-162 .


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