First Responders: Automated Accident Detection & Response System - Mini Project for 6th Sem (FRMP6)

FRMP6 is a multi-functional security system integrating AI-based monitoring, real-time networking, cloud storage, and encryption to enhance surveillance and tracking capabilities.

This project is developed for research and academic purposes.

It utilizes YOLOv8 for AI-based object detection and tracking. Additionally, the system tracks the closest responder and hospital for emergency response. The cloud deployment is done using Google Colab, though it is not included in this repository.

Project Structure

The project consists of four main components, each focusing on a critical aspect of the system:

1. AI/ML Component (aiml/)

This module powers AI-driven surveillance features such as object detection and face recognition.

• mainsec.py & img.py - Core AI scripts for security analysis.
• best.pt - Trained deep learning model for detection.
• fr/images/training/ - Dataset used for training AI models.
• aiml_security.log - Logs AI-based security operations.
• encryption_key.key - Possibly used for securing AI-related data.

2. CCTV Monitoring (cctv/)

Responsible for real-time video surveillance using connected cameras.

• Manages live camera feeds and recording functionalities.
• Facilitates secure storage and streaming of video footage.

3. UI/UX (uiux/)

The user interface provides an interactive dashboard to monitor and manage security features.

• Contains HTML, CSS, PHP, and JavaScript files.
• Allows users to view CCTV feeds, access alerts, and control system settings.

4. GPS Tracking (gps/)

This component tracks the real-time location of assets or individuals.

• Provides location-based alerts and geofencing capabilities.
• Integrates with the UI for visual representation on a map.

Prerequisite Technologies

In addition to the dependencies listed in requirements.txt, the following technologies are essential for the project:

• Python (for AI/ML and backend processing)
• YOLOv8 (for object detection and tracking)
• Flask / Django (for backend services)
• OpenCV (for image and video processing)
• Google Colab (for cloud-based model training and execution)
• JavaScript, HTML, CSS, PHP (for UI development)
• MySQL / Firebase (for database and storage management)

Installation & Setup

1. Install dependencies from requirements.txt:

   pip install -r requirements.txt

2. Configure the environment as needed.
3. Run the respective scripts to start AI monitoring, video surveillance, UI, or GPS services.

Contributors

This project was developed by a team of five members, with each contributor focusing on a specific module.
Contributors: leafycodes, anwesha-24, shubhub29, AnaghDas