Intelligent surveillance system will automatically recognise and tracking faces with the help of image processing. https://github.com/abiz95/intelligent_surveillance_system

1. INTELLIGENT
SURVEILLANCE
SYSTEM
Presented by
Abin T John
Agil Mathews

2. Objective
 Intelligent surveillance system has received growing attention
due to the increasing demand on security and safety.
 The intelligent surveillance system is capable of recognizing
faces in real time.
 The recent developments in sensor devices, computer vision,
and machine learning have an important role in enabling such
intelligent system.
 Intelligent surveillance system will automatically recognize
faces and track it on live video streams from surveillance
cameras in public or commercial places.

3. Introduction
 Intelligent surveillance system is envisioned to
automatically monitor the environment or
infrastructure with less or without human
intervention.
 Such monitoring tasks include automatically
detecting and tracking people and performing
further analysis and actions.
 Signal processing, image processing, and artificial
intelligence (machine learning) techniques play
important role to develop such intelligent system.

4. Existing systems
 Visible camera such as CCTV is the most
common modalities (device) for surveillance
system.
 It has long been in use to monitor
environments, people, events and activities.
 Extensive analysis is required for studing the
visual data which is time intensive and
requires a lot of human effort.
 It is an extremely inefficient process.

5. Functional Block Diagram

6. Start
Admin Login
New
User?
Add user
Start surveillance
Need to
play
playback?
Playback
Search in database Facebase
Display
result
Close
Methodology

7. Advantages
 It requires less amount of time to analysis the footages.
 It is not labor intensive.
 It is cost efficient.
 Easy to process large visual data’s.

8. Disadvantages
 Accuracy of algorithm
 Accuracy of face recognition during night

9. Results
GUI
 Login page

10. • Add user

11. • Index page

12. • Start

13. • Playback

14. • Search

15. Future Works Possible
 Number plate recognition along with face recognition to track
vehicles.
 Targeted advertisement .
 Robust object detection to track objects in the environment.
 More robust algorithm for better recognition.

16. GitHub Link
 Use the link to the project files
https://github.com/abiz95/intelligent_surveillance_system

17. Reference
 Robert T CollinsFFet al. A system for video Surveillance and
Monitoring. Technical Report CMU-Rl-TR-00-12,Carnegie Mellon
University , 2006.
 Mekenna T. Video surveillance and human activity recognition for anti-
terrorism and force protection[J]. Proceedings of IEEE Conference on
Advanced Video and Signal Based Surveillance, 2003(6):22-23.
 Crow Franklin, "Summed-area tables for texture mapping”, in
Proceedings of SIGGRAPH, Conference on Computer Graphics and
Interactive Techniques, Volume 18, No. 3, ISBN: 0-89791-138-5, pp. 207-
212, July 1984.
 Paul Viola, Michael Jones, „Rapid object detection using a boosted
cascade of simple features”, Computer Vision and Pattern Recognition,
Proceedings of the 2001 IEEE Computer Society Conference on, vol.1, pp.
I-511- I-518, 2001.
 DD Nguyen and HS Le, "Kinect Gesture Recognition: SVM vs. RVM,"
2015 Seventh International Conference on Knowledge and Systems
Engineering (KSE), Ho Chi Minh City, 2015, pp. 395-400

18. Reference
 Ruiquan Ge , Zhenfang Shan, Hao Kou et.al: ‘An Intelligent
Surveilance system based on motion detection’ Department of Express
and Logistics, Shijiazhuang Posts and Telecommunications Technical
College, Shijiazhuang 050021.
 Y. M. Mustafah, T. Shan, A. W. Azman, A. Bigdeli, B. C. et.al: ‘Real time
face detection and Tracking for higher resolution Smart camera system’
NICTA Ltd, 300 Adelaide Street, Brisbane, QLD 4000, Australia b ITEE,
university of Queensland, Brisbane, QLD 4072, Australia
 Yao-Jiunn Chen, Yen-Chun Lin, „Simple Face-detection Algorithm
based on Minimum Facial Features”, Industrial Electronics Society,
IECON 2007, 33rd Annual Conference of the IEEE, pp. 455-460,
November 2007.
 Petre Anghelescu, Ionut Serbanescu, Silviu Ionita et.al: ‘Surveillance
system using IP camera and Face Detection algorithm’, Department of
Electronics, Communications and Computers,University of Pitesti Pitesti,
Romania, VOL.19,
 P Viola and MJ Jones, “Robust Real Time Face Detection”, International
Journal of Computer Vision, Vol. 57, No. 2, 2004, pp.137-154

19. Thank You