SmartTraffic AI — Vision Edition
Inspiration
Urban traffic congestion causes delays, increases fuel consumption, and makes road management more difficult. We wanted to explore how computer vision and artificial intelligence could help make traffic monitoring more intelligent, measurable, and efficient.
SmartTraffic AI — Vision Edition extends our interest in AI-powered traffic management by focusing on visual analysis of road traffic.
What the Project Aims to Do
The project aims to analyze traffic videos and extract useful information about road conditions. Our planned features include:
- Vehicle Detection: Identify vehicles in traffic footage using a computer-vision model.
- Vehicle Counting: Count detected vehicles and summarize traffic flow.
- Congestion Estimation: Estimate traffic density using vehicle counts and configurable thresholds.
- Visual Analytics: Display detection results, traffic statistics, and congestion summaries in a web dashboard.
- Cloud Integration: Use Amazon Web Services (AWS) for a meaningful cloud-based component, such as storing processed reports or annotated snapshots.
How We Plan to Build It
The project will use Python and OpenCV 5 for image and video processing, with a suitable pretrained object-detection model for vehicle recognition. FastAPI will provide backend endpoints, and React with Vite will power the web dashboard. AWS will support selected cloud functionality.
We plan to test the system on sample traffic videos and evaluate vehicle-detection results, counting accuracy, and processing performance.
Learning Goals and Challenges
This project is an opportunity to explore practical computer vision, AI model integration, video processing, cloud deployment, and full-stack development. Key challenges include handling different lighting conditions, avoiding duplicate vehicle counts, managing processing speed, and estimating congestion reliably.
We aim to address these challenges through incremental implementation, testing, and measurable evaluation.
Expected Impact
SmartTraffic AI — Vision Edition aims to demonstrate how accessible computer-vision technology can turn traffic footage into useful information for traffic analysis and urban mobility research.
The project is intended as a prototype for analysis and decision support, not a replacement for certified traffic-control systems.
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