1. About this file
This ReadMe.txt is attached to the German Traffic Sign Detection Benchmark (GTSDB) dataset. It was presented
in a competition at the IEEE International Joint Conference for Neural Networks (IJCNN) 2013. Please visit
http://benchmark.ini.rub.de
for further details. You are free to use this data package for any purpose you like.
If you use it in a scientific publication, we want to ask you to cite the following paper:
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing and Christian Igel,
"Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark",
IEEE International Joint Conference on Neural Networks (submitted), 2013
BibTeX:
@inproceedings{Houben-IJCNN-2013,
author = {Sebastian Houben and Johannes Stallkamp and Jan Salmen and Marc Schlipsing and Christian Igel},
booktitle = {International Joint Conference on Neural Networks (submitted)},
title = {Detection of Traffic Signs in Real-World Images: The {G}erman {T}raffic {S}ign {D}etection {B}enchmark},
year = {2013},
}
2. Content of the download package
Along with this file you should have received a zip-file containing ...
a) 900 image files with natural traffic scenes 00000.ppm - 00899.ppm
b) a text file gt.txt containing the ground truth for all traffic signs in the images
c) image sections with single traffic signs in the respective subdirectories named after the IDs (see below)
3. Explanation of ground truth text file
The text file contains lines of the form
#ImgNo#.ppm;#leftCol#;##topRow#;#rightCol#;#bottomRow#;#ClassID#
for each traffic sign in the dataset. The first field refers to the image file the traffic sign is located in. Field 2 to 5 describe
the region of interest (ROI) in that image. Finally, the ClassID is an integer number representing the kind of traffic sign.
The mapping is as follows:
0 = speed limit 20 (prohibitory)
1 = speed limit 30 (prohibitory)
2 = speed limit 50 (prohibitory)
3 = speed limit 60 (prohibitory)
4 = speed limit 70 (prohibitory)
5 = speed limit 80 (prohibitory)
6 = restriction ends 80 (other)
7 = speed limit 100 (prohibitory)
8 = speed limit 120 (prohibitory)
9 = no overtaking (prohibitory)
10 = no overtaking (trucks) (prohibitory)
11 = priority at next intersection (danger)
12 = priority road (other)
13 = give way (other)
14 = stop (other)
15 = no traffic both ways (prohibitory)
16 = no trucks (prohibitory)
17 = no entry (other)
18 = danger (danger)
19 = bend left (danger)
20 = bend right (danger)
21 = bend (danger)
22 = uneven road (danger)
23 = slippery road (danger)
24 = road narrows (danger)
25 = construction (danger)
26 = traffic signal (danger)
27 = pedestrian crossing (danger)
28 = school crossing (danger)
29 = cycles crossing (danger)
30 = snow (danger)
31 = animals (danger)
32 = restriction ends (other)
33 = go right (mandatory)
34 = go left (mandatory)
35 = go straight (mandatory)
36 = go right or straight (mandatory)
37 = go left or straight (mandatory)
38 = keep right (mandatory)
39 = keep left (mandatory)
40 = roundabout (mandatory)
41 = restriction ends (overtaking) (other)
42 = restriction ends (overtaking (trucks)) (other)
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traffic-sign-detection-master.zip_sign detection_svm 交通标志_svm
共672个文件
mat:610个
m:26个
png:24个
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利用SVM模型来完成对交通标志进行检测和识别
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traffic-sign-detection-master.zip_sign detection_svm 交通标志_svm (672个子文件)
f_display_roi.asv 362B
.gitignore 17B
sampleImages.jpg 334KB
+vecolorsamples.jpg 80KB
mandatory_svm_result.jpg 48KB
danger_svm_result.jpg 31KB
f_template_matching.m 4KB
TSD_readGTData.m 3KB
f_generateColorTrainingData.m 2KB
io_readTxtFile.m 2KB
f_generate_recognition_training_data.m 2KB
f_generateNegativeTrainingData.m 2KB
f_generate_recognition_testing_data.m 2KB
f_hand_pick_color.m 2KB
s_generate_recognition_test_data.m 2KB
f_hog.m 1KB
s_apply_svm_to_data.m 1KB
s_generate_recognition_training_data.m 1KB
s_get_rois.m 1KB
bm_getJaccardCoefficient.m 908B
f_generate_color_features.m 836B
s_train_recognition_svm.m 791B
s_generateNegativeTrainingData.m 641B
f_apply_svm.m 523B
s_flip_and_resize_templates.m 508B
f_train_recognition_svm.m 381B
f_display_roi.m 369B
f_mygradient.m 309B
s_readData.m 292B
f_train_svm.m 278B
s_test_hue_sat_hist.m 143B
f_flip_and_resize.m 138B
recognitonModels.mat 3.74MB
00001.mat 2.78MB
mandatory_SVMModel.mat 497KB
negativeSamples.mat 134KB
00099.mat 48KB
00389.mat 46KB
00338.mat 44KB
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