GeoTagging License Plates of Nigeria Using Computer Vision

Local Chapter Kaduna, Nigeria Chapter

Coordinated byNigeria ,

Status: Completed

Project Duration: 30 May 2023 - 31 Jul 2023

Open Source resources available from this project

Project background.

Our project is driven by the vision to redefine the use of CCTV systems for enhanced security measures. Recognizing this need, we aim to develop a unique API that uses deep learning technologies to identify motorists who have failed to renew their vehicle registration, as well as to track stolen vehicles. The system will leverage information such as license plate numbers and previously GeotTagged locations to accomplish these tasks. This innovative application not only contributes to improved public safety but also facilitates effective law enforcement.

The problem.

The rising incidence of vehicle theft presents an ongoing challenge that calls for real-time tracking and monitoring solutions. Our project seeks to address this by using an API that enables instantaneous tracking of vehicles based on their geolocations. Moreover, there is a significant issue concerning vehicle owners failing to renew their registration with the Inland Revenue. By integrating our system with the database, we can effectively identify and track these defaulters, thereby facilitating regulation enforcement and promoting public safety.

Project goals.

The project goals are: Enhancing Motorist Security, Enabling Real-Time Tracking, Establishing Vehicle Travel Timeline, and Amplifying the Functionality of State CCTV Systems.

Project plan.

  • Week 1

    Data collection and preprocessing

  • Week 2

    Modelling and database

  • Week 3

    API creation

  • Week 4

    Deployment and visualization

  • Week 5

    Testing and enhancing the API

Learning outcomes.

Learn computer vision, Geospatial data and machine learning algorithms with database applications in Python.

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