🏠 Smart Home Automation with Computer Vision & Deep Learning

An intelligent home automation system integrating object detection, activity recognition, and hardware control.

📌 Overview

This project implements a Smart Home Automation System using computer vision and deep learning to enhance home security, convenience, and automation. By leveraging OpenCV, YOLOv8 Nano, and Pyfirmata, the system can recognize specific activities—such as detecting when a person sits on a chair—and trigger corresponding hardware actions.

🎯 Features

✅ Real-time Object Detection – Identifies people and objects using YOLOv8 Nano.

✅ Activity Recognition – Detects specific human actions (e.g., sitting on a chair).

✅ Automated Hardware Control – Uses Pyfirmata to interact with IoT devices.

✅ Enhanced Home Security – Monitors activity for intelligent security measures.

✅ Adaptive Environment Interaction – Adjusts home automation settings based on detected activities.

🛠 Technologies Used

1. OpenCV – Computer vision processing

2. YOLOv8 Nano – Lightweight deep learning-based object detection

3. Pyfirmata – Arduino-based hardware control

4. Python – Core programming language

🚀 Getting Started

Install Dependencies: pip install opencv-python ultralytics pyfirmata

Run the System: python smart_home.py

Observe real-time detection and automated hardware responses!

🔥 Future Enhancements

🔹Integration with voice assistants (e.g., Alexa, Google Assistant).

🔹 Expansion to more household activities and appliances.

🔹 Cloud-based monitoring and logging for remote access.

💡 Bringing intelligence to home automation with deep learning and computer vision! 🚀