Crowd-Analyzer

Amir Rafe | Feb 24, 2025 min read
Crowd-Analyzer

Crowd-Analyzer: An Open-Source Tool for Advanced Crowd Behavior Analysis

Understanding crowd movement and behavior is crucial in areas like urban planning, emergency evacuation, and public safety. Crowd-Analyzer is an open-source tool that combines state-of-the-art computer vision with scientific pedestrian analytics. Built on YOLO object detection, ByteTrack/BotsSort tracking, and PedPy analysis framework, this tool provides researchers, engineers, and urban planners with a comprehensive platform for extracting meaningful insights from video footage of pedestrian and crowd movement.

What is Crowd-Analyzer?

Crowd-Analyzer is a Python-based tool that processes video footage to detect, track, and analyze crowd behavior. It leverages state-of-the-art deep learning models to:

  • Detect individuals in a crowd with high accuracy.
  • Track movement patterns over time using robust object tracking methods.
  • Extract key statistics such as density, flow rate, and directionality of movement.
  • Provide visualizations for better interpretation of crowd dynamics.
  • Support real-time and offline processing for flexibility in analysis.


Key Features

1️⃣ Advanced Detection & Tracking

  • YOLO-based detection (yolo11x, yolo11l, yolo11m, yolo11s, yolo11n)
  • Robust tracking with ByteTrack or BotsSort, enhanced by Kalman filter for smooth trajectories
  • Real-time visualization with color-coded tracks & unique IDs
  • Configurable confidence & IoU thresholds
  • Homography-based world coordinate transformation

2️⃣ Multi-Method Trajectory Analysis

  • PedPy integration for advanced analytics
  • Speed & density calculations using Voronoi and Classic methods
  • CSV export & customizable analysis parameters
  • Kalman filter-based motion prediction & smooth trajectory estimation

3️⃣ AI-Powered Analysis & Visualization

  • PyQt6-based GUI with real-time processing & automated plots
  • AI-powered interpretation via Groq LLM for density, speed, & trajectory insights
  • Scientific explanations of observed patterns & automated report generation
  • Multi-tab interactive dashboard for dynamic density, speed, & trajectory analysis

4️⃣ Comprehensive Processing & Outputs

  • Interactive homography-based coordinate transformation & distance calibration
  • Real-time detection, tracking state transitions (NEW → TRACKED → LOST), & historical track visualization
  • Automated data export with statistical plots & expert-level AI insights


How to Get Started

Setting up Crowd-Analyzer is straightforward. Follow these steps:

1️⃣ Clone the repository:

git clone https://github.com/pozapas/Crowd-Analyzer.git

2️⃣ Install required dependencies:

pip install -r requirements.txt

3️⃣ Launch the application:

python CrowdAnalyzer.py

4️⃣ Using the GUI:

  • Click “Load Video” to select your input video
  • Configure settings through the Settings panel:
    • Select YOLO model (yolo11x/l/m/s/n)
    • Choose tracking algorithm (ByteTrack/BotsSort)
    • Set confidence and IoU thresholds
    • Configure frame rate and analysis parameters
  • Click “Start Processing” to begin analysis
  • Results will be automatically saved to the specified output folder

Potential Applications

🔹 Urban Planning & Transportation: Analyze pedestrian flow to optimize sidewalk design, public transportation hubs, and crosswalk placements.

🔹 Emergency Management & Evacuation Planning: Improve evacuation strategies by understanding movement patterns in high-density areas.

🔹 Event Management & Public Safety: Assist security teams in monitoring crowd congestion and potential hazards in large gatherings such as concerts, festivals, and stadium events.

🔹 Retail & Commercial Spaces: Understand customer foot traffic to enhance store layouts and marketing strategies.

🔹 AI and Robotics: Develop autonomous systems that interact intelligently with human movement patterns in crowded environments.

Future Improvements

The development of Crowd-Analyzer is ongoing, with planned enhancements such as:

⬜ Enhanced tracking accuracy using transformer-based models.

⬜ Support for live video streaming to analyze events in real-time.

⬜ Multi-camera tracking for large-scale event surveillance.

⬜ Advanced anomaly detection to identify unusual behavior patterns automatically.

Contribute & Collaborate

Crowd-Analyzer is a community-driven project. Contributions are welcome, whether it’s improving detection algorithms, optimizing performance, or adding new features. Developers, researchers, and urban planners are encouraged to collaborate and expand the tool’s capabilities.

💡 Want to contribute? Check out the GitHub repository for open issues, discussions, and development roadmap!

Final Thoughts

Crowd-Analyzer bridges the gap between AI-driven computer vision and practical crowd analysis. With its robust detection, tracking, and visualization capabilities, it offers a valuable tool for researchers, urban planners, emergency responders, and event organizers.

Whether you’re optimizing urban spaces, planning for large-scale evacuations, or studying human behavior, Crowd-Analyzer provides powerful insights into crowd dynamics.

Try it out, contribute, and let’s push the boundaries of crowd behavior analysis together!

comments powered by Disqus