The document discusses the motivation and design of an automatic number plate recognition system to track vehicles entering and exiting a premises. The key goals of the system are to: 1) Maintain logs of entry/exit times and photos of vehicles, 2) Extract license plate numbers from photos using ANPR, and 3) Provide information like the number of vehicles currently inside and counts over specific time periods. It then describes the hardware components, software, and image processing methods used to achieve these goals in a low-cost and effective manner when deployed at the LUMS campus.

2. The motivation for our project stems from a demand in the market to develop a system which can:
• Maintain an online log for entry/exit time of vehicles in a certain premises (factory etc)
• Maintain an online record with pictures for vehicles entering/exiting the premises
• Extract the number from these pictures through ANPR(Automatic Number Plate Recognition)
• ANPR should work on all legal number plates within Pakistan. E.g. Lahore, Karachi and
Islamabad number plates
• Provide complete record of vehicles inside a premises at any given time, time of entry and exit
of vehicle, setting up an action upon entry or exit of a vehicle (SMS etc – future additions)
• Provide vehicle count for past 10, 30 and 60 minutes.

3. • Maintains a data for the entry/exit time of vehicles.
• Solves administrative issues by generating a text log for vehicles entering/ exiting
the premises. This helps keep track of vehicle inflow, outflow and no. of vehicles in
the premises which is helpful in fleet management.
• The system has a provision of keeping a record for a given period of time (e.g. 1
month).
• The system allows the user to ascertain the status of vehicles online, to know
whether the vehicle is present in the premises or not.
• Provides a fast, efficient and reliable method of identifying presence of vehicles in a
premises compared to traditional methods

5. Sends Signal
Trigger
Camera
Send Image to
Raspberry Pi
To ANPR
System
Extracted
NumberTo server

6. • Vehicle detection
• Database development and online log
• Number plate Identification via ANPR

8. Loop Specifications
1. 40/76 wire
2. 4 turns
3. Rectangular Octagon
4. Twisted cable

10. • Change in voltage transmitted as car passes over

11. • Voltage change ~0.2V
• Amplification of voltage change using ADC
• Setting upper and lower threshold
• Extensive testing to ensure no false readings
𝑈𝑝𝑝𝑒𝑟 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

12. • Image initially saved in RaspberryPI
• Image later transferred to Server using SSH communication
• Automatically run code every time RaspberryPI restarts using crontab
• Image name taken to be date and time i.e. 2016-04-22.09:00:00.jpeg

13. • Assign a static IP to both devices
• Use keyless SSH entry for communication between RaspberryPI and Server
• Generate public keys for both devices and share public keys among them
• Using crontab, send all images saved in RaspberryPI to Server every minute
• Delete image from RaspberryPI after sending image to Server
• Allows user access to Raspberry PI from Server

14. • Images recived in html folder of Server, where website is being hosted
• Installation of following softwares:
• Apache
• PHP
• html
• Mysql
• Phpmyadmin; GUI for databases
• Nodejs
• traffic.lums.edu.pk

19. • Storing name directly into database
• Breaking name to get time and date and storing it into database
• Make four variables:
• Start_time; gives initial time
• end_time; gives end time
• date_start;
• date_end;

21. Image from Current Position Image from Up Close

22. Gray Scale Image

23. Binary image

24. Bounding Boxes plotted around the connected components

25. LED 7658 LE 5033
Objects Extraction using Template Matching

26. Image from Current Position Image from Up Close

27. Identification of the Connected Components

28. LEB 8936 LEB 9493
Objects Extraction using Template Matching

29. The analysis on the mentioned methods leads to following conclusion:
• The camera must be of high quality
1.3MP Night Vision Waterproof License ANPR License Plate Capture(LPR) 1080P IP Camera
Plate Recognition Camera
US $ 275.00 US $255.00

30. • Camera Positioning
• Ideal position is at road level
• Single camera covering one lane
• Distance of the camera from the vehicle should be kept minimum

31. The system was deployed at the out gate of LUMS. The following were included in it:
1. Webcam
2. Raspberry Pi (controller/client)
3. Loop controlling circuit

32. ITEM COST(PKR)
Raspberry Pi 5000
Webcam 8000
Wire (for loop) 1000
Labor 700
Hardware 1600
Total 16300

33. • No initial cost and logistics required to paste sticker on every vehicle
• No need to bring all vehicles within the system for system to work

34. • The proposed design is a prototype of cost effective solution to address an
important administrative issue. Potential future work on this project includes:
• Application of same system on the entrance of LUMS.
• Improved image processing techniques for number plate identification
• An SMS alert system to inform arrival/exit of vehicle of choice