Bus Crowd Detection System

Managing crowd levels in public buses is a challenge for many cities around the world. Overcrowding can lead to safety concerns, discomfort, and delays. To help address this issue, I've developed a Bus Crowd Detection System using computer vision and email notifications for real-time crowd monitoring. This system not only detects people in a bus but also sends out notifications when it detects a crowd, allowing for proactive management and improved passenger safety. Here's how it all works.

Github

Key Features of the Bus Crowd Detection System

The Bus Crowd Detection System is packed with features designed to monitor and respond to real-time crowding:

1. Real-Time Person Detection: By leveraging the efficiency of YOLOv4-tiny, the system can detect people in a video feed from either a webcam or an IP camera.

2. Email Notifications: If the system detects an excessive crowd, it automatically sends an email notification to a predefined recipient.

3. Data Storage for Analysis: Detections are logged in an SQLite database so that you can track crowding over time and analyze patterns.

4. Configurable Notification Cooldown: Avoids spamming by setting a custom cooldown period between email alerts.

5. Secure Credential Management: Uses environment variables to store email credentials securely.

Prerequisites for Setting Up the System

To build and run this project, you’ll need:

• Python 3.7 or higher

• A Webcam or IP camera

• Gmail account (with App Password enabled for secure access)

• Git to clone the project repositor

Step-by-Step Installation Guide

Here’s how to set up and run the Bus Crowd Detection System:

Clone the Repository Start by cloning the GitHub repository to your local system:

git clone https://github.com/Neelpatel1604/Person-Detection

2. Install Required Packages

Next, navigate to the project directory and install the necessary Python packages:

cd bus-crowd-detection

pip install -r requirements.txt

3. Set Up Email Credentials For the system to send email notifications, create a .env file in the root directory and add your email details as environment variables:

SENDER_EMAIL=your_email

SENDER_PASSWORD=your_app_password

RECIPIENT_EMAIL=recipient_email

Make sure your Gmail account has an App Password enabled, as this provides a more secure way to access your account.

Download YOLOv4-Tiny Weights

YOLOv4-tiny requires specific weights to perform accurate detection. Follow these steps:

Create a models folder in the project root.

Download the yolov4-tiny.weights, Coco name , yolov4-tiny.cgf file and place it into models folder

Or Coco name and yolov4-tiny.cgf will be in your system when you clone the repo.

Running the System

Once everything is set up, you can start the detection system:

python main.py

While the program is running:

• Press 'q' to quit the program.

• The detection runs automatically, with bounding boxes displayed around detected people.

• Email notifications are triggered based on the set crowd threshold.

How the System Works:Features in Detail

Person Detection with YOLOv4-Tiny YOLOv4-tiny is a streamlined version of the YOLOv4 model, designed for faster detection with minimal loss of accuracy. When running, the system detects people in real-time, drawing bounding boxes around them and showing a confidence score to indicate detection reliability.

Email Notifications for Real-Time Alerts One of the most valuable aspects of this system is its ability to send email alerts when the crowd level reaches a certain threshold. This feature is configurable and prevents multiple emails from being sent in a short period through a cooldown period, giving administrators actionable insights without overload.

Data Logging with SQLite For historical data analysis, each detection is stored in an SQLite database with a timestamp, count, and occupancy rate. This data enables you to track crowding patterns and make decisions based on data trends.

Troubleshooting Common Issues

While setting up, you might encounter some common issues. Here are some tips for resolving them:

• Email Not Sending:

  • Confirm your Gmail App Password is correct.

  • Check your internet connection.

  • Ensure your .env file is correctly set up with all the required variables.

• Detection Not Working:

  • Make sure your camera is properly connected and functioning.

  • Confirm that yolov4-tiny.weights is in the models folder.

  • Improve lighting for better detection accuracy.

• Database Errors:

  • Check write permissions on the database file.

  • Ensure SQLite is installed and working on your system.

Understanding the Project Structure

Here’s a quick look at the key files in the project:

• main.py: The entry point to run the system.

• crowd_detection.py: Contains the logic for detecting people.

• db_handler.py: Manages database operations.

• requirements.txt: Lists the Python packages needed to run the system.

• .env: Stores sensitive environment variables (like email credentials).

Security Recommendations

This project uses email credentials to send notifications, so it's essential to follow these security tips:

• Never share your .env file with others.

• Use Gmail App Passwords for enhanced security.

• Keep your model files secure to prevent unauthorized modifications.

Contributing to the Project

If you’d like to add features or improvements, contributions are always welcome! Here’s how you can contribute:

1. Fork the repository.

2. Create a new branch for your feature.

3. Submit a pull request with a brief description of your changes.

Real life implementation

1. Install Cameras on Buses: Set up a webcam or IP camera inside each bus to capture real-time video footage of passengers.

2. Deploy YOLOv4-tiny Model: Use the YOLOv4-tiny model for person detection. This lightweight model is ideal for real-time processing and works efficiently on edge devices like Raspberry Pi or embedded systems.

3. Set Up a Detection Server: Run the detection software on a server or embedded device onboard the bus. This will handle video processing, person detection, and occupancy calculations.

4. Database Integration: Store detection data, including bus ID, passenger count, occupancy rate, and timestamp, in a local SQLite database or cloud storage for analysis.

5. Email Alerts: Configure email notifications to alert the fleet manager when the bus exceeds a certain occupancy rate or crowd threshold.

6. Monitor and Optimize: Use the stored data to optimize bus routes, schedules, and manage crowding, improving passenger safety and comfort.

Conclusion

With this Bus Crowd Detection System, you can monitor bus occupancy in real time, allowing for faster responses to overcrowding and improved safety for all passengers. Whether you’re managing a public transportation fleet or just interested in computer vision projects, this system offers a practical and easy-to-implement solution for real-world challenges.