Hierarchical Traffic Sign Recognition Based on Multi-feature and Multi-classifier Fusion

DOI

10.2991/iset-15.2015.15How to use a DOI?

Keywords

Traffic sign recognition, Multi-feature fusion, Multi-classifier fusion

Abstract

In this paper, we propose a fast and robust method for traffic sign recognition, which uses a coarse-to-fine strategy. The traffic signs are divided into main category and sub-category. At the coarse classification stage, we extract histogram of oriented gradients (HOG) feature from different spectral bands of traffic sign images and classify into main category using a linear support vector machine (SVM). Then at the fine classification stage, complementary features of dense-sift, local binary pattern (LBP) and Gabor filter features are extracted, fused and then fed to a committee of SVM and random forest. The proposed method gets an accuracy of 98.76% on the German Traffic Sign Recognition Benchmark (GTSRB) dataset and takes about 50ms per image. Both recognition accuracy and speed is higher than that of the method based on multi-scale convolutional neural network.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yunxiang Ma
AU  - Linlin Huang
PY  - 2015/03
DA  - 2015/03
TI  - Hierarchical Traffic Sign Recognition Based on Multi-feature and Multi-classifier Fusion
BT  - Proceedings of the First International Conference on Information Science and Electronic Technology
PB  - Atlantis Press
SP  - 56
EP  - 59
SN  - 2352-538X
UR  - https://doi.org/10.2991/iset-15.2015.15
DO  - 10.2991/iset-15.2015.15
ID  - Ma2015/03
ER  -