https://github.com/erenyeager101/crowd_monitoring
Crowd monitoring and management using real-time data from IP camera and Laptop camera footage which aims to provide users with insights into the crowd density at various locations espicially at local market places , shops ,malls. This helps users make informed decisions about visiting places based on the level of crowdiness.
https://github.com/erenyeager101/crowd_monitoring
crowd-monitoring opencv pyth
Last synced: 8 days ago
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Crowd monitoring and management using real-time data from IP camera and Laptop camera footage which aims to provide users with insights into the crowd density at various locations espicially at local market places , shops ,malls. This helps users make informed decisions about visiting places based on the level of crowdiness.
- Host: GitHub
- URL: https://github.com/erenyeager101/crowd_monitoring
- Owner: erenyeager101
- Created: 2024-06-21T11:40:17.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T17:26:21.000Z (25 days ago)
- Last Synced: 2025-04-17T07:17:43.841Z (13 days ago)
- Topics: crowd-monitoring, opencv, pyth
- Language: HTML
- Homepage: https://crowd-monitoring-hack2skill.vercel.app/
- Size: 95.8 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# 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
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Data Collection](#data-collection)
- [UI and Visualization](#ui-and-visualization)
- [Server-Side Functionality](#server-side-functionality)
- [Contributing](#contributing)
- [License](#license)
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## 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.
```bash
git clone https://github.com/erenyeager101/Crowd_monitoring.git
cd Crowd_monitoring
```
Ensure you have all dependencies installed by running:
```bash
dependencies.bat
```
## Usage
To start the application, run the main script in the root directory:
```bash
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.
```python
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:
```bash
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/`