Computer Vision Based Automatic Toll Management System


Computer Vision Based Automatic Toll Management System

100 in stock

SKU: Computer Vision - Toll Management System Category:


Abstract :

                Electronic toll collection (ETC) system has been a common trend used for toll collection on toll road nowadays. The implementation of electronic toll collection allows vehicles to travel at low or full speed during the toll payment, which help to avoid the traffic delay at toll booth. One of the major components of an electronic toll collection is the automatic vehicle detection and classification (AVDC) system which is important to classify the vehicle so that the toll is charged according to the vehicle classes. Vision-based vehicle classification system is one type of vehicle classification system which adopts RF as the input sensing device for the system. The vehicle identification is by using number plate recognition. In database we are having each vehicle numbers.

               This project includes the development of an image-based vehicle classification system as an effort to seek for a robust vision-based vehicle classification system. This project uses Raspberry Pi for implementation. The Raspberry Pi is a credit card sized single computer or SoC uses ARM1176JZF-S core. SoC, or System on a Chip, is a method of placing all necessary electronics for running a computer on a single chip. It needs an Operating system to start up. SD/MMC card will acts as a bootable hard disk.


Existing System :

  • No tracking system
  • No identification  of vehicle type
  • No clustering technique

Proposed System :

  • Tracking implementation based on classification
  • Identifying vehicle type
  • Clustering and feature extraction is used 

Block Diagram :

                                      Computer Vision Based Automatic Toll Management System 1

Vehicle Section :

                                                Computer Vision Based Automatic Toll Management System 2

Hardware units:

  •   Raspberry Pi
  •   SD card
  •   Monitor


  • Raspbian
  • Language – Linux , Embedded C

Additional information

Weight 1.000000 kg


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