交通标志牌识别样本数据及相应算法

数据来源于：珍惜原创成果~

http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset

• • Home
  • GTSRB
    • News
    • About
    • Dataset
    • Results
    • Schedule
    • Submissions
    • Contact
  • GTSDB

        

  

  

  

  

   

   

   

   

   

   

   

   

   

  Dataset

  Overview

  Structure

  Image format

  Annotation format

  Result format

  Pre-calculated features

  
  

   

  HOG features

  
  The file contains three sets of differently configured HOG features (Histograms of Oriented Gradients). The sets contain feature vectors of length 1568, 1568, and 2916 respectively. The features were calculated using the source code fromhttp://pascal.inrialpes.fr/soft/olt/. For detailed information on HOG, we refer to
  
  N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition, pages 886-893, 2005
  
  

  Haar-like features

  
  The file contains one set of Haar-like features. For each image, 5 different types of Haar-like features were computed in different sizes for a total of 12 different features. The overall feature vector contains 11,584 features.
  

   

  Hue Histograms

  
  For each image in the training set, the file contains a 256-bin histogram of hue values (HSV color space).
  

   

   

  Code snippets

  
  Matlab

  
  The Matlab example code provides functions to iterate over the datasets (both training and test) to read the images and the corresponding annotations.
  Locations where you can easiliy hook in your training or classification method are marked in the code by dummy function calls.
  Please have a look at the file Readme.txt in the ZIP file for more details
  

   

  C++

  
  The C++ example code demonstrates how to to train a linear classifier (LDA) using theShark machine learning library.
  This code uses the precalculated features. It was used to generate the baseline results.
  Please have a look at the file Readme.txt in the ZIP file for more details
  

   

  Python

  
  The Python example code provides a function to iterate over the training set to read the images and the corresponding class id.
  The code depends on matplotlib. Please have a look at the fileReadme.txt in the ZIP file for more details
  

  
  

  Citation

  The data is free to use. However, we cordially ask you to cite the following publication if you do:
  
  J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. InProceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460. 2011.
  
  

  @inproceedings{Stallkamp-IJCNN-2011,    author = {Johannes Stallkamp and Marc Schlipsing and Jan Salmen and Christian Igel},    booktitle = {IEEE International Joint Conference on Neural Networks},    title = {The {G}erman {T}raffic {S}ign {R}ecognition {B}enchmark: A multi-class classification competition},    year = {2011},    pages = {1453--1460}}

  Thank you.
  
  

  Result Analysis Application

  We provide a simple application to facilitate result analysis. It allows you to compare different approaches, analyse the confusion matices and inspect which images were classified correctly.
  The software is supplied under GPLv2. It depends on Qt 4.7, which is available here in source code and binary form. Qt is licensed under LGPL. Qt is a trademark of Nokia Corporation.
  
  The software is provided as source code and Win32 binary. The files can be found in thedownload section. The code is platform-independent, however, it has only been tested on Microsoft Windows with Visual Studio. So there might be a couple of issues left where GCC is more strict than Visual Studio. We appreciate any comments, patches and bug reports.
  
  The project uses CMake, an open-source, cross-platform build system which allows you to generate project files/makefiles for your preferred compiler toolchain.
  
  Here are some screenshots to get an idea of this tool.
  

  
  
  
  Main window: Performance of one or more approaches
  
  
  
  
  Compare multiple approaches and on which images they erred
  
  
  
  
  Confusion matrix: See which classes got confused.
  
  
  
  
  Clicking cells, rows or columns in the confusion matrix shows which images were misclassified.
  Here: All "Speed limit 60" images that were incorrectly classified as some other class.
  
  

  Downloads

  Training dataset

  
  This is the official GTSRB training set. If you either intend to participate in the final competition session at IJCNN 2011 or you want to publish experimental results based on GTRSB data, you must use this dataset for training.

  
  The training data set contains 39,209 training images in 43 classes.

  • Images and annotations: Download (263 MB)
  • Three sets of different HOG features: Download (870 MB)
    
  • Haar-like features: Download (944 MB)
    
  • Hue histograms: Download (17 MB)
    

   

  Test dataset

  
  Thís is the official GTSRB test set. It was first published at IJCNN 2011 during the special session "Traffic Sign Recognition for Machine Learning". All experimental results that are reported on GTSRB data must use this dataset for testing (apart from the ones already published at IJCNN 2011). The structure of the dataset follows the test set that was published for the online competition (and is now part of the training data).
  

  The test dataset contains 12,630 test images or the corresponding pre-calculated features in random order.

  • Images and annotations: Download (84 MB)
  • Three sets of different HOG features: Download (278 MB)
    
  • Haar-like features: Download (304 MB)
    
  • Hue histograms: Download (5 MB)
    
  • Extended annotations including class ids: Download (98 kB)