  In this work we developed a novel and fast traffic sign recognition system, a
very important part for advanced driver assistance system and for autonomous
driving. Traffic signs play a very vital role in safe driving and avoiding
accident. We have used image processing and topic discovery model pLSA to
tackle this challenging multiclass classification problem. Our algorithm is
consist of two parts, shape classification and sign classification for improved
accuracy. For processing and representation of image we have used bag of
features model with SIFT local descriptor. Where a visual vocabulary of size
300 words are formed using k-means codebook formation algorithm. We exploited
the concept that every image is a collection of visual topics and images having
same topics will belong to same category. Our algorithm is tested on German
traffic sign recognition benchmark (GTSRB) and gives very promising result near
to existing state of the art techniques.
