CrowdNav

Analyzing real-time crowd density using video processing and pathfinding algorithms to guide users through the most efficient route

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Inspiration

Crowds can be unpredictable, making navigation difficult and, in some cases, even dangerous. Whether it’s a paramedic trying to reach a patient, an event staff member managing logistics, or an individual experiencing distress, finding an efficient way through a dense crowd is a challenge. Traditional navigation tools don’t account for real-time crowd density, which can lead to delays or unsafe routes. CrowdNav was built to solve this problem—using real-time video feeds to identify open pathways and dynamically compute the best possible route through the crowd.

What it does

CrowdNav processes overhead camera feeds to detect crowd density and movement patterns in real-time. Using computer vision techniques, it generates a heatmap where areas of high density are assigned higher weights, while open pathways remain low-cost routes. The system then applies Dijkstra’s algorithm to compute the most efficient path between a user-defined starting point and destination. As the crowd moves, CrowdNav recalibrates the route dynamically, ensuring the path remains optimal at any given moment. Users can adjust their start and endpoints at any time, and the system instantly adapts to provide the safest and quickest path through the crowd.

How we built it

Frontend (React.js): Provides an intuitive interface where users can select their desired route and visualize the optimal path in real time.
Backend (Python & Flask): Manages image processing, heatmap generation, and pathfinding, ensuring seamless communication between the frontend and real-time data.
Computer Vision (OpenCV): Processes camera feeds to detect crowd movement, analyzing each frame individually then passed by our algorithm to generate precise movement-density maps.
Pathfinding (Dijkstra’s Algorithm): Assigns weights based on crowd density and heat maps given by our algorithm, to provide the most efficient path.
Dynamic Recalibration: The system continuously updates the optimal as new crowd movement data is processed, ensuring real-time accuracy.

Challenges we ran into

Real-time processing: Ensuring that video data is analyzed efficiently without significant lag was a key challenge.
Variable camera conditions: Different lighting, camera angles, and resolutions impacted detection accuracy, requiring adaptable preprocessing techniques.
Path optimization: Finding a balance between accuracy and computational speed while ensuring smooth, human-friendly navigation.

Accomplishments that we're proud of

We have successfully integrated real-time computer vision with dynamic pathfinding, creating a system that adapts to live crowd movement, ensuring efficient and safe navigation. CrowdNav’s ability to continuously recalibrate in real-time makes it highly responsive to changing crowd conditions, maintaining optimal routes at all times.

What we learned

We optimized computer vision models for real-time crowd detection, fine-tuning them to handle varying conditions and ensure accurate analysis of camera feeds. We also implemented and refined Dijkstra’s algorithm for dynamic, weighted pathfinding, incorporating crowd density to calculate the most efficient routes.

What's next for CrowdNav

Machine Learning & Predictive Modeling: Enhancing crowd detection accuracy by training models on diverse datasets and leveraging historical data with live trends to forecast crowd movements and suggest proactive routing solutions.
Multi-Camera Support: Combining feeds from multiple angles to provide a more complete view of an area.
Server-Client Expansion: Transitioning CrowdNav to a client-based system, allowing for seamless real-time guidance in large, crowded areas by distributing the processing load between the server and client.
Emergency System Integration: Partnering with first responders to help paramedics and security teams navigate crowds efficiently.

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