Hierarchical Traffic Sign Recognition Based on Multi-feature and Multi-classifier Fusion
- Yunxiang Ma, Linlin Huang
- Corresponding Author
- Yunxiang Ma
Available Online March 2015.
- https://doi.org/10.2991/iset-15.2015.15How to use a DOI?
- Traffic sign recognition, Multi-feature fusion, Multi-classifier fusion
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - First International Conference on Information Science and Electronic Technology (ISET 2015) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/iset-15.2015.15 DO - https://doi.org/10.2991/iset-15.2015.15 ID - Ma2015/03 ER -