- An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine
- Author: Zhiyong Huang, Yuanlong Yu, Jason Gu, and Huaping Liu
- Published: IEEE TRANSACTIONS ON CYBERNETICS, 2017.
The proposed method consists of two modules:
- Extraction of histogram of oriented gradient variant (HoGv) feature
- A single classifier trained by extreme learning machine (ELM) algorithm
Step 1. Run the create_test_data_in_HoGv.m and create_train_data_in_HoGv.m to create the database of feature.
Step 2. Run the main.m to train the model and store the weighting of ELM (TSR_GTSRB.mat).
Step 3. Load the weighting and then inference.
Opensource Database: GTSRB
The training data(39209 images,43classes) :
- Images and annotations (GTSRB_Final_Training_Images.zip)
The test dataset(12630 images) :
- Images and annotations (without ground truth classes) (GTSRB_Final_Test_Images.zip)
- Extended ground truth annotations (with classes) (GTSRB_Final_Test_GT.zip)
The dimenson of HoGv feature is fixed ,2500.
All experiments were carried out in a Matlab R2019a environment running on a desktop PC with a 3.8 GHz AMD Ryzen 5 3600X 6-Core Processor and a 16 GB memory.
- The Accuracy and time of training phase are 99.99% and 6.1ms/frame.
- The Accuracy of testing phase are 96.63%.