Intelligent Traffic Perception
Real-time traffic monitoring using deep learning for smarter and safer roads.
Created on 24th February 2025
•
Intelligent Traffic Perception
Real-time traffic monitoring using deep learning for smarter and safer roads.
The problem Intelligent Traffic Perception solves
This project uses deep learning to enhance real-time traffic monitoring by analyzing video feeds to detect multiple objects such as vehicles, pedestrians, traffic signs, and lanes. Unlike traditional systems that are limited to single-category detection or outdated sensors, this approach provides comprehensive, structured data. This data can help city planners, law enforcement, and autonomous vehicle systems make better decisions, improving road safety and traffic efficiency in increasingly urbanized and congested environments.
Challenges we ran into
Model Chaining Complexity: Integrating two YOLO models (one for object detection and another for road segmentation) required optimizing inference time to maintain real-time performance. We overcame this by using parallel processing and optimized model weights.
Video Processing Efficiency: Handling live video feeds efficiently without WebSockets was challenging. We used batch processing and optimized frame selection to ensure smooth real-time detection.
Deployment Issues: Hosting the FastAPI application with real-time processing on a cloud server required optimizing resource usage. We resolved this by using asynchronous processing and GPU acceleration where available.
Road Segmentation Issues: training for masking instead of boxes set us out of our comfort zones.
Tracks Applied (1)