Hierarchical Traffic Sign Recognition for Autonomous Driving

Vartika Sengar, Renu Rameshan, Senthil Ponkumar

2020

Abstract

Traffic Sign Recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recognized with very high accuracy. It is generally observed that traffic signs have large intra-class variability and small inter-class variability. This makes visual distinguishability between distinct classes extremely irregular. In this paper we propose a hierarchical classifier in which the number of coarse classes is automatically determined. This gives the advantage of dedicated classifiers trained for classes which are more difficult to distinguish. This is an application oriented work which involves systematic and intelligent combination of machine learning and computer vision based algorithms with required modifications for designing fully automated hierarchical classification framework for traffic sign recognition. The proposed solution is a real-time scalable machine learning based approach which can efficiently take care of wide intra-class variations without extracting desired handcrafted features beforehand. It eliminates the need for manually observing and grouping relevant features, thereby reducing human time and efforts. The classifier performance accuracy is surpassing the accuracy achieved by humans on publicly available GTSRB traffic sign dataset with lesser parameters than the existing solutions.

Paper Citation

in Harvard Style

Sengar V., Rameshan R. and Ponkumar S. (2020). Hierarchical Traffic Sign Recognition for Autonomous Driving. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 308-320. DOI: 10.5220/0008924703080320

in Bibtex Style

@conference{icpram20,
author={Vartika Sengar and Renu Rameshan and Senthil Ponkumar},
title={Hierarchical Traffic Sign Recognition for Autonomous Driving},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={308-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008924703080320},
isbn={978-989-758-397-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Hierarchical Traffic Sign Recognition for Autonomous Driving
SN - 978-989-758-397-1
AU - Sengar V.
AU - Rameshan R.
AU - Ponkumar S.
PY - 2020
SP - 308
EP - 320
DO - 10.5220/0008924703080320