A novel pLSA based Traffic Signs Classification System

TL;DR

This paper presents a fast traffic sign recognition system using pLSA and image processing techniques, combining shape and sign classification to improve accuracy for driver assistance and autonomous driving.

Contribution

It introduces a novel pLSA-based approach with a bag of features model for multiclass traffic sign classification, demonstrating promising results on a benchmark dataset.

Findings

Achieved near state-of-the-art accuracy on GTSRB dataset

Utilized pLSA for effective multiclass classification

Combined shape and sign classification for improved performance

Abstract

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.