Traffic Sign Classifier For Self Driving Cars

Shaik Nageena Jasmine,  Gampala Santhosh, Karna Yoga Deepika; Dr. G. Prasuna

doi:10.62647/

Authors

Shaik Nageena Jasmine, Gampala Santhosh, Karna Yoga Deepika U. G Student, Dept Computer Science And Engineering, St.Ann’s College Of Engineering and Technology, Nayunipalli (V), Vetapalem (M), Chirala, Bapatla Dist, Andhra Pradesh – 523187, India. Author
Dr. G. Prasuna Associate professor, Computer Science And Engineering, St.Ann’s College Of Engineering and Technology, Nayunipalli (V), Vetapalem (M), Chirala, Bapatla Dist, Andhra Pradesh – 523187, India. Author

DOI:

https://doi.org/10.62647/

Keywords:

Convolutional neural network (CNN), OpenCV, Image processing, Histogram of Oriented Gradients (HOG), Support Vector Machine (SVM), Colour detection.

Abstract

The project titled “Traffic sign classifier for self-driving cars”, In this project, we are using computer vision-based techniques like machine learning and deep learning that uses convolutional neural network and support vector machine for color detection and image classification that detect the traffic signal. In present generation the autonomous vehicles are raised day by day. Its primary purpose is to detect the traffic signal using the live camera, capture the state, detect the light (yellow, red, green) and send the signal to self-driving cars for safety and efficient driving. This project will help us to reduce the accidents and Challenging conditions such as poor lightening, occlusions and weather distortions.