Automatic Traffic Rule Violation Detection and Fine Collection Using Machine Learning

This project aims to develop a system that automatically detects traffic rule violations and facilitates fine collection using machine learning techniques. This system is designed to improve road safety and streamline the process of monitoring traffic rule adherence.

Table of Contents

• Introduction
• Objectives
• Features
• Technologies & Libraries Used
• Installation

Introduction

The project focuses on leveraging machine learning algorithms to detect traffic rule violation i.e., helmet detection. The system captures and processes video feeds from traffic cameras, identifies violations, and sends notifications to the relevant authorities for fine collection.

Objectives

• To develop a machine learning model that accurately detects traffic rule violations.
• To automate the process of fine collection for traffic violations.

Features

• User-friendly interface for monitoring and managing violations.
• Automatic notification system for fine collection.

Technologies & Libraries Used

• Python
• YOLOv5 and OpenCV (for helmet detection)
• OCR and ALPR API (for Licence Plate Recognition)
• Tkinker (for frontend)
• Yagmail (for sending challan to the violator)

Installation

1. Clone the repository

git clone https://github.com/omkarjavali/Automatic-Traffic-Rule-Violation-Detection-and-Fine-Collection-Using-Machine-Learning.git

2. Navigate to project directory

cd Automatic-Traffic-Rule-Violation-Detection-and-Fine-Collection-Using-Machine-Learning

3. Install the required dependencies

pip install -r requirements.txt

4. Run the project

python StartProject.py