Description:
This project implements a Smart Traffic Management System using Automatic Number Plate Recognition (ANPR) and Automatic Traffic Classification and Control (ATCC) to automate traffic monitoring and analysis in smart city environments.
The system was developed during the Infosys Springboard Internship as a real-world application of computer vision and deep learning concepts.
What the system does:
- Detects and tracks vehicles from traffic video footage
- Recognizes vehicle license plates using ANPR
- Classifies traffic into different vehicle categories (ATCC)
- Generates structured CSV data for traffic analysis
- Interpolates missing detections to improve accuracy
- Produces annotated output videos for visualization
Why it matters:
- Enables automated traffic monitoring
- Helps reduce manual intervention in traffic analysis
- Supports data-driven traffic planning and control
- Demonstrates scalable smart city use cases
Tech Stack:
- Programming: Python
- Computer Vision: OpenCV
- Deep Learning: Object Detection models (YOLO)
- Data Processing: CSV generation and interpolation
- Visualization: Annotated traffic videos
Internship Certificate:
Completed as part of the Infosys Springboard Internship.
Domain: Artificial Intelligence / Computer Vision
