🚦 Traffic Violation Detection System (ML + Computer Vision)

A hybrid Machine Learning + Computer Vision system that detects traffic violations from live or recorded video feeds.
It identifies vehicles, detects violations (red-light jump, overspeeding, wrong-lane usage, etc.), logs them with time, image, and license plate, and generates daily reports.

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🧠 Overview

Goal:
To build a complete end-to-end intelligent system that can:

• Detect vehicles using YOLOv8 + OpenCV
• Track vehicle movements in real-time
• Identify violations using ML classifiers (Random Forest, Decision Tree, Logistic Regression)
• Log violation details with timestamps and cropped images
• Visualize data using Streamlit Dashboard or FastAPI

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⚙️ Tech Stack

Layer	Tools / Libraries
Detection & Vision	OpenCV, YOLOv8 (Ultralytics)
Machine Learning	scikit-learn, pandas, numpy
OCR (License Plate)	EasyOCR, Tesseract (optional)
Dashboard & Reporting	Streamlit / FastAPI, Matplotlib
Database / Storage	SQLite3, CSV
Version Control	Git & GitHub

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🧩 Folder Structure

traffic-violation-detector/ ├── data/ # Input videos and datasets ├── logs/ # Saved violations, cropped images ├── models/ # YOLO weights / trained ML models ├── src/ │ ├── ml_models/ # ML models (.pkl) │ ├── database.py # Handles SQLite logging │ ├── detection.py # YOLOv8 detection logic │ ├── tracking.py # Object tracking logic │ ├── features.py # Feature extraction for ML │ └── main.py # Main control script ├── requirements.txt # All dependencies └── README.md # Project documentation

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🚀 Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/siddhant1729/traffic-violation-detector.git
cd traffic-violation-detector
2️⃣ Create Virtual Environment
bash
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python -m venv venv
.\venv\Scripts\activate       # On Windows
source venv/bin/activate      # On Linux/Mac
3️⃣ Install Requirements
bash
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pip install -r requirements.txt
4️⃣ Run the System
bash
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python src/main.py
🎯 Features
✅ Vehicle Detection using YOLOv8
✅ Object Tracking using OpenCV (CSRT / DeepSORT)
✅ Violation Detection

Red Light Jump

Wrong Lane

Overspeeding
✅ ML Integration for intelligent classification
✅ License Plate Recognition (OCR)
✅ SQLite Logging + Image Capture
✅ Streamlit Dashboard / FastAPI API
✅ Daily Report Generation

📊 Example Output (Coming Soon)
Annotated video showing detected violations

Daily CSV report

Streamlit dashboard screenshots

👨‍💻 Author
Siddhant
CSE Undergrad | Competitive Programmer | AI/ML Enthusiast
📍JIIT Noida


🏁 License
This project is open-source and available under the MIT License