A novel pLSA based Traffic Signs Classification System
This addresses traffic sign classification for advanced driver assistance and autonomous driving, but it is incremental as it builds on existing methods.
The authors tackled traffic sign recognition using a pLSA-based system with image processing and bag-of-features, achieving results near state-of-the-art on the GTSRB benchmark.
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.