FalconSight
4 Weeks•Developer•3 team members
FalconSight Demo

FalconSight is an AI-powered drone-based emergency detection system that leverages deep learning, computer vision, and real-time video processing to identify critical situations such as human distress, SOS gestures, and emergency text signs. Designed for rapid response in inaccessible areas, FalconSight combines mini-drone technology with secure communication protocols for efficient surveillance and disaster management.

Deep Learning
Computer Vision
OCR
Mini-Drone Technology
Real-time Video Processing
WiFi Modules
Gesture Recognition

FalconSight 🚀

An AI-powered drone-based emergency detection system

Overview

FalconSight revolutionizes emergency response by integrating mini-drones with AI-driven computer vision to detect emergencies in real-time. This system enhances surveillance and disaster management, ensuring rapid response in critical situations.

Key Features

  • Real-time Video Processing: High-speed data transmission for immediate analysis.
  • Human Detection: AI-driven deep learning models identify people in distress.
  • SOS Gesture Recognition: Temporal pattern matching enables accurate recognition of emergency signals.
  • Text Detection (OCR): Reads and interprets emergency messages with advanced preprocessing.
  • Mini-Drone Technology: Lightweight drones equipped with HD cameras and WiFi modules.
  • Secure Communication: Ensures reliable transmission of critical data.

Why FalconSight?

FalconSight bridges the gap in emergency response, offering an innovative AI-powered aerial surveillance system capable of identifying threats in real time. It is particularly effective in disaster-stricken and remote areas where conventional monitoring systems fail.

Prerequisites

  • Python 3.7 or higher
  • Flask, OpenCV, EasyOCR, and other dependencies listed in requirements.txt

Getting Started

  1. Clone the repository:

    bash
    git clone https://github.com/RAGAV203/Drone-Cam-Analysis.git
cd Drone-Cam-Analysis

  2. Set up a virtual environment (recommended):

    bash
    python -m venv venv
source venv/bin/activate  # On Windows, use `venvScriptsactivate`

  3. Install dependencies:

    bash
    pip install -r requirements.txt

Running the Application

  1. Start the Flask App:

    bash
    python app.py

  2. Open your browser and navigate to http://127.0.0.1:5000 to access the app.

Project Structure

  • app.py: The main Flask application file that runs the server and sets up the routes.
  • detector.py: Contains the core code for gesture detection using OpenCV and OCR using EasyOCR.
  • requirements.txt: Lists all the dependencies required to run the application.

Usage

  1. Gesture Detection & OCR Functionality: The app detects various gestures from the overlay window on the Analysis Region. The detector.py file processes video frames and identifies gestures using OpenCV.
  2. To adjust the region to analyze: Change the below line in app.py file 
    bash
    scrcpy_bbox = {'top': 450, 'left': 600, 'width': 720, 'height': 320}  # Adjust to match screen setup

Dependencies

This project uses the following libraries:

  • Flask: To create the web server.
  • OpenCV: For handling video and image processing.
  • EasyOCR: For optical character recognition.
  • MSS: Screen Recording.

Installation

All dependencies are listed in requirements.txt. Run the following command to install them:

bash
pip install -r requirements.txt