metadata

title: INTRUSITON
sdk: docker
emoji: 🏃
colorFrom: red
colorTo: yellow

Intrusion Detection System

Overview

The Intrusion Detection System is designed to monitor environments using computer vision techniques. It can process real-time video feeds or uploaded images to detect potential intrusions and other relevant activities. The system utilizes YOLOv8, a state-of-the-art object detection model, to analyze video streams and images for detection purposes.

Features

Real-Time Video Feed: Monitors live video from a webcam or camera for immediate detection.
Image Upload: Allows users to upload images for detection.
Intrusion Detection: Utilizes YOLOv8 for accurate detection of intruders and relevant objects.
User-Friendly Interface: Simple and intuitive interface for selecting video or image upload options.

Technologies Used

Flask: Web framework for building the application.
OpenCV: Library for computer vision tasks.
YOLOv8: Object detection model used for analyzing video and images.
HTML/CSS/JavaScript: Frontend technologies for building the user interface.

Installation

Prerequisites

Python 3.9

Clone the Repository

git clone https://github.com/yourusername/intrusion-detection.git

Install Dependencies

pip install -r requirements.txt

Model File

Make sure to download the YOLOv8 model file (yolov8n.pt) and place it in the project directory.

Running the Application

Start the Flask server:

python app.py

Open a web browser and navigate to http://localhost:5000.
Choose between real-time video feed or image upload to detect intrusions.

Usage

Real-Time Video Feed: Click the "Real-Time Video Feed" button to start the video stream from your camera. Use the "Play" and "Pause" buttons to control the video feed.
Upload Image: Click the "Upload Image" button to select an image file from your device and get detection results.