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Crowd Monitoring and Management

Crowd monitoring and management using real-time CCTV footages as well as video footages from different sources which aims to provide users with insights into the crowd density at various locations espicially at local market places , shops ,malls.This proposed system will able to predict crowd on timel-basis and forecast the same on timely basis. This helps users make informed decisions about visiting places based on the level of crowdiness.

Table of Contents

(Web Development Frameworks

)

Overview

The goal of this project is to track and manage crowd density in real-time using CCTV footage. The system detects the number of people at a location, provides an option to input coordinates, and displays the crowd data on a map. It also shows peak and average crowd levels, using color coding to indicate crowd intensity.

Features

  • Real-time detection of crowd density using IP camera and laptop camera footage(Prototype)
  • Input of coordinates for location-specific monitoring
  • Display of crowd data on a map with color coding (red for high crowd, yellow for low crowd)
  • Calculation and display of max_crowd, average_crowd, and preffered_shop
  • UI enhancements for a user-friendly experience

Installation

To set up the project, clone the repository and install the required dependencies.

git clone https://github.com/erenyeager101/Crowd_monitoring.git
cd Crowd_monitoring

Ensure you have all dependencies installed by running:

dependencies.bat

Usage

To start the application, run the main script in the root directory:

start.bat

Access the web interface at http://localhost:3000 and follow the on-screen instructions to view and interact with the crowd data.

Data Collection

The system uses IP camera on android device or laptop camera footage to detect the number of people at a specific location. This data, along with coordinates and IP address, is sent to the server to update the map with the crowd information.

UI and Visualization

The project includes a visually appealing and user-friendly interface. The map visualization helps users easily identify crowded areas and make decisions accordingly.

import matplotlib.pyplot as plt
import seaborn as sns


plt.figure(figsize=(10, 6))
sns.barplot(x=locations, y=crowd_levels)
plt.xlabel("Locations")
plt.ylabel("Crowd Levels")
plt.show()

Server-Side Functionality

The server processes the incoming data, updates the crowd information on the map, and calculates the max_crowd, average_crowd, and preffered_shop values. It also provides real-time updates to the UI.

Contributing

Contributions are welcome! Please create a pull request or raise an issue to discuss your ideas. Ensure that your contributions follow the project's coding standards and guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Additional Setup Instructions

  1. Dependencies Installation:

    • All requirements are added in the dependencies.bat file. To install all dependencies, simply run this .bat file in the terminal.
    • After running the dependencies.bat file, add your own IP address in the detection.py file. To find the IP address, install the "IP Camera" app from the Play Store. Once the server starts on the IP Camera app, the IP address will be displayed.

  2. Running the Project:

    • To run the project, navigate to the project directory in the terminal and run the command: 
      start.bat
    • Ensure that the IP Camera server is started on your mobile device before running the project.
    • Point the camera to a crowd to count the number of people.

  3. Current progress and issues faced -We tried to deploy this project but due to lack of resources we cant although we improved the UI/UX of the website pretty much but due to time congestions we couldnt we have attached the deployment of our sample frontend of how this project would look like in future https://vite-woad-two-83.vercel.app/