Here's a GitHub description and documentation for your Crowd Management System project:

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AI-Powered Crowd Management System

Description

The Crowd Management System is a cutting-edge solution leveraging Computer Vision and AI to analyze and manage crowd density in real time. This system uses video footage to detect, track, and estimate the size and movement of crowds, helping to ensure safety, optimize space utilization, and streamline operations in public spaces.

Features:

• Real-time crowd density analysis and heatmap generation.
• People counting and movement tracking.
• Alerts for overcrowding or unusual activity detection.
• Scalable for various environments, including stadiums, malls, and public events.

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Tags

#CrowdManagement #ComputerVision #AI #RealTimeAnalytics #DeepLearning 
#PublicSafety #CrowdDensity #ObjectDetection #YOLO #SecuritySystem

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Documentation

1. Prerequisites

Install the required dependencies:

pip install opencv-python tensorflow keras numpy matplotlib

2. Running the Project

1. Clone the repository:

   git clone https://github.com/bashitalishaikh/crowd-management-system.git

2. Navigate to the project folder:

   cd crowd-management-system

3. Run the main script:

   python crowd_management.py

3. Key Components

• Crowd Detection Module: Uses YOLO or SSD for object detection to count people in real-time.
• Density Estimation Module: Generates heatmaps to visualize crowd density.
• Alert System: Sends alerts when predefined crowd thresholds are exceeded.

4. Models Used

• YOLOv5: Efficient and accurate object detection for real-time applications.
• DeepSORT: For multi-object tracking to analyze movement patterns.

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Future Aspects

1. Advanced Analytics:

   • Predictive modeling for crowd flow and bottleneck detection.
   • Integrate with weather and event data for better crowd prediction.

2. Smart City Integration:

   • Deploy in smart cities for public safety and event management.
   • Connect with IoT devices for enhanced data collection and real-time insights.

3. Mobile and Edge Computing:

   • Develop mobile-friendly versions for on-the-go monitoring.
   • Optimize for edge devices to reduce latency and improve speed.

4. Extended Use Cases:

   • Emergency evacuation planning in case of disasters.
   • Integration with law enforcement systems for improved security.

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Contribution

Contributions are welcome!
Feel free to submit pull requests, report issues, or suggest features.

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Contact

Maintainer: [Your Name]

• LinkedIn
• Email
• Repository: GitHub Link

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