Traffic-Intersection-Monitoring/Week1.md

GSOC-25: Traffic Intersection Monitoring with OpenVINO

This project develops a real-time system to detect traffic objects at intersections. It uses YOLOv11 and YOLOv12 deep learning models optimized with OpenVINO to identify vehicles, pedestrians, and traffic signs efficiently on Intel hardware.

Current Progress (Week 1)

• Built the main detection pipeline
• Tested different YOLO models for accuracy and speed
• Created vehicle classification based on size and shape
• Developed image processing and visualization tools
• Added tracking to maintain object consistency between frames
• Implemented filtering to remove false positives and overlapping detections

FeaturesD:\Downloads\finale6\khatam\qt_app_pyside

• Train custom YOLOv12n models using traffic data from the COCO dataset
• Convert models from PyTorch format to OpenVINO IR format
• Quantize models to INT8 for faster inference without losing accuracy
• Run detection on images, video files, and webcam streams
• Detect common traffic classes such as cars, trucks, pedestrians, and traffic lights
• Deploy models on CPU, GPU, and other OpenVINO-supported devices