The document proposes a smart city traffic control system using video processing and the Internet of Things. The system would dynamically control traffic lights based on traffic density measured by video processing from Raspberry Pi cameras. Video would also be stored in the cloud for surveillance purposes. Traffic updates would be provided over the Internet using IoT to allow users to check traffic density. The system aims to reduce traffic jams and wait times using automatic, dynamic traffic control based on real-time video analysis.

1. SMART CITY TRAFFIC
CONTROL SYSTEM USING
VIDEO PROCESSING AND
INTERNET OF THINGS
Vikas Singh Rajput (UR12EC253)
Sunil Kumar Singh (UR12EC238)
Titus M Mathews (UR12EC247)
Shrikant Mahapatra (UR12EC228)
Guide:- Mrs. Bella Mary

2. Objective
• To design a system which controls traffic
light on the basis of traffic density using
video processing.
• Store videos at cloud which can be used for
surveillance purpose.
• Traffic update on Internet using IoT.

3. Existing Traffic Control System
• Manual Controlling
• Automatic Controlling
• Heavy Traffic Jams
• No traffic, but still need to wait
Limitations

4. Proposed project
• A system is proposed in this project which
controls traffic dynamically according to the
density using video processing. If a
particular lane is highly dense then it will be
given more time to flow the traffic otherwise
less time will be given.
• The video recorded is also storing at cloud
which can be used for surveillance purpose.
• The traffic density is also updated on Internet
in regular interval of time and this can be
done by using IoT.

5. Advantage of proposed project
• Less time wastage.
• Automatic and dynamic control of Traffic.
• Video can be used for surveillance.
• User can check traffic density on Internet.

6. Block Diagram

7. Modules
 Video processing done by Raspberry Pi.
 Make a cloud storage for video storage.
 Control the traffic lights using Raspberry.
 Traffic update on Internet using IoT.

8. Flowchart

9. Result
Original Frame

10. Background using Frame Subtraction

11. Foreground Abstraction

12. After Morphological Transform

13. Blob Detection

14. Output

15. Traffic Update using Internet of
Things

16. Traffic Update of four lanes

17. OwnCloud

18. Storage of video in cloud

19. References
 MathWorks
 Wikipedia
 What-when-how.com
 Tutorialspoint.com
 Raspberrypi.org
 Opencv.org
 Github.com

20. THANK
YOU