CrowdGuard AI

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  zone management

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  dashboard

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  survillance monitor

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  ai camera analysis

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  Analytics

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  simulation

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  events

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  privacy & ethics

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  sos & contacts

Inspiration

Large gatherings—concerts, festivals, sports events, rallies—are exciting but can quickly become unsafe when crowd density rises or movement becomes chaotic. Tragic incidents around the world have shown how quickly overcrowding can escalate into crushes or stampedes. We wanted to create a tool that could predict danger before it happens and give organizers a simple, real-time way to monitor crowd safety using the cameras they already have.

What it does

CrowdGuard AI uses real-time video analysis to detect people in a scene, estimate crowd density, monitor movement patterns, and assign a risk level (low/medium/high). When the app detects potential overcrowding or irregular flow, it generates alerts so security teams or event organizers can act early. Detects humans in live video feeds

Tracks movement and identifies clusters

Estimates density and flow

Highlights high-risk zones

Generates visual overlays for clear understanding

Sends early warnings to prevent crowd disasters

How we built it

How we built it

We built CrowdGuard AI using an AI-driven computer-vision pipeline:

Object Detection – A deep learning model (e.g., YOLO) identifies people in each frame.

Tracking – A multi-object tracker (e.g., ByteTrack) follows people across frames to understand movement.

Perspective Transform – Converts the camera view into a top-down “bird’s-eye view” for accurate spacing/density calculations.

Clustering & Density Analysis – Algorithms like DBSCAN group individuals and estimate crowd intensity.

Risk Scoring System – Combines density, clustering, and motion to calculate a color-coded risk level.

Frontend Dashboard – A browser-based interface (your app) displays the processed video along with live metrics.

We used Python, OpenCV, and deep-learning inference on the backend and created an intuitive web UI for monitoring.

Challenges we ran into

Making detection accurate in low-light or crowded scenes

Handling occlusion when people overlap heavily

Calibrating the perspective transform for different camera angles

Optimizing performance so the system runs in real time without lag

Designing a user interface that gives clear insight without overwhelming the user

Accomplishments that we're proud of

Successfully building a real-time crowd-monitoring system from scratch

Achieving stable human detection and tracking even in busy areas

Creating a clean UI highlighting risk levels and crowd metrics

Reducing computational load enough to run smoothly on typical hardware

Turning a safety-focused idea into a functional, deployable app

What we learned

How to combine computer vision, tracking, and clustering into one pipeline

The importance of perspective correction for reliable spatial analysis

How challenging real-time video processing can be

How small variations in camera placement dramatically affect detection quality

How to design a safety tool that balances accuracy with speed

What's next for CrowdGuard AI

Multi-camera fusion for tracking crowds across larger areas

Mobile app alerts for security personnel

Heatmap visualizations of crowd flow

Integration with drones or PTZ cameras

Predictive modeling to forecast dangerous density before it forms

Privacy-focused features like auto-blurring faces or anonymization

A full analytics dashboard for event organizers

Built With

• base44
• opencv
• python
• yolov8