🚗 Vehicle Detection and Tracking System

A side project crafted to master computer vision and software engineering practices. This system detects, tracks, and counts vehicles in real-time using YOLOv8 for object detection and SORT for multi-object tracking — all built with a clean, maintainable architecture that follows the SOLID principles.

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✨ Key Features

• 🧠 Smart Object Detection
  Detects cars, trucks, buses, motorcycles, and bicycles using YOLOv8 with high accuracy and speed.

• 🎯 Reliable Multi-Object Tracking
  Uses the SORT algorithm to consistently track vehicles across video frames, even with partial occlusions.

• 🔢 Accurate Vehicle Counting
  Counts each vehicle once as it crosses a virtual counting line, preventing duplicates.

• 🖼️ Real-Time Visualization
  Renders bounding boxes, unique IDs, and the live count directly on the video feed.

• 🧼 Clean Code with SOLID Principles
  The codebase is modular, maintainable, and adheres to best practices like separation of concerns and object-oriented design.

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📦 Requirements

• Python 3.8 or higher
• OpenCV
• NumPy
• cvzone
• ultralytics (YOLOv8)
• scikit-image (used by SORT)
• filterpy

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🚀 Installation

1. Clone the repository:

   git clone https://github.com/faris771/Vehicle-Detection-and-Tracking-System.git
   cd vehicle-tracking
   

2. Install dependencies:

  pip install -r requirements.txt

3. Run the script:

   python3 main.py

🧠 Motivation

This project was developed as part of my journey to master computer vision fundamentals and improve my Python architecture skills. It merges machine learning, real-time systems, and clean software engineering.

📬 Contact

Feel free to reach out via LinkedIn or open an issue if you have feedback or suggestions!