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While outdoor navigation systems like Google Maps have become highly advanced, finding efficient paths indoors such as inside hospitals, airports, or shopping malls remains a major challenge. Most existing indoor navigation tools are static, meaning they cannot adapt to real-time changes such as crowded areas or accessibility barriers. They also rarely account for the diverse needs of users, including those with mobility limitations.
This project aims to solve these challenges by developing a computer vision-based, congestion-aware indoor pathfinding system. Using camera feeds and advanced algorithms, the system will predict crowd density in real time and generate optimized routes that adapt dynamically as conditions change.
The innovation of this project lies in three main aspects:
– Real-time Congestion Prediction: Instead of only finding the shortest path, the system uses computer vision to detect and forecast crowded areas, automatically adjusting routes to save time and reduce frustration.
– Personalized Accessibility Integration: The system tailors routes to user preferences such as avoiding stairs, construction zones, or tight spaces ensuring safe and inclusive navigation for all, especially for individuals with mobility challenges.
– System-level Optimization: Beyond individual users, the model distributes foot traffic intelligently across multiple routes, reducing bottlenecks and improving overall flow in large indoor spaces.
In practical use, this technology will reduce travel time, improve comfort and accessibility, and enhance the efficiency of crowded environments like hospitals, transit hubs, and malls. By combining real-time adaptability with user-centered inclusivity, the project delivers a next-generation indoor navigation solution that addresses current gaps and contributes to smarter, more accessible public spaces.
Behrouz Far
Edmonton Unlimited
Engineering
Professional, scientific and technical services; Public administration
University of Calgary
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