Real-Time Intrusion Detection using OpenCV & Deep Learning | Python + Source Code

🚀 Welcome to this hands-on project – Real-Time Intrusion Detection using OpenCV and Deep Learning!

In this tutorial, you’ll learn how to build an AI-powered security system that can detect intrusions in real time using Python, OpenCV, and Deep Learning models.

Whether you’re a student, developer, or AI enthusiast — this project will help you understand computer vision, object detection, and real-time video analytics used in modern security systems.

👉 By the end of this video, you’ll learn how to:
✅ Capture and process live video streams using OpenCV
✅ Detect and classify intruders using Deep Learning models (YOLO / CNN)
✅ Implement motion and object detection in real time
✅ Automate alerts when suspicious movements occur
✅ Integrate AI with Python for smart security monitoring

⚙️ Technologies & Tools Used:

Python 🐍

OpenCV 👁️

TensorFlow / Keras 🤖

Deep Learning (CNN / YOLO) 🧠

Numpy, Pandas, Matplotlib

Real-Time Video Feed Integration

💡 Why Watch This Video?
This project is ideal for:
🔹 AI & Deep Learning beginners
🔹 Computer Vision enthusiasts
🔹 Final-year students building academic projects
🔹 Developers interested in smart surveillance & intrusion detection systems

🕒 Intrusion Detection System Project Timeline (Approx. 24 Minutes)
00:00 - 01:30 → Introduction and Project Overview (Intrusion Detection Importance, CCTV Security)

01:31 - 04:00 → Setting Up Project Environment and Folder Structure

04:01 - 07:00 → Installing Libraries and Importing Modules (Ultralytics, OpenCV, Tkinter, NumPy)

07:01 - 10:00 → Explanation of Input Videos, Polygon Coordinates, and Model Files

10:01 - 13:00 → Model Loading and Evaluation Setup (YOLOv8 Model for Person Detection)

13:01 - 16:30 → Video Frame Processing, Object Detection, and Sorting

16:31 - 20:00 → Intrusion Detection Logic: Region Monitoring and Time Thresholds

20:01 - 22:30 → GUI Implementation: Start/Stop Monitoring, Polygon Drawing, Log Display

22:31 - 24:00 → Running Demo Inputs, Final Outputs, Summary, and Closing Remarks

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