Year: 2015
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Haloi, Mrinal
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1597629222