DocumentCode
3497951
Title
Traffic sign classification using K-d trees and Random Forests
Author
Zaklouta, Fatin ; Stanciulescu, Bogdan ; Hamdoun, Omar
Author_Institution
Robot. Center, Mines ParisTech, Paris, France
fYear
2011
fDate
July 31 2011-Aug. 5 2011
Firstpage
2151
Lastpage
2155
Abstract
In this paper, we evaluate the performance of K-d trees and Random Forests for traffic sign classification using different size Histogram of Oriented Gradients (HOG) descriptors and Distance Transforms. We use the German Traffic Sign Benchmark data set [1] containing 43 classes and more than 50,000 images. The K-d tree is fast to build and search in. We combine the tree classifiers with the HOG descriptors as well as the Distance Transforms and achieve classification rates of up to 97% and 81.8% respectively.
Keywords
gradient methods; pattern classification; random processes; traffic engineering computing; transforms; tree data structures; German traffic sign benchmark; HOG descriptor; K-d tree; classification rate; distance transform; histogram of oriented gradients; random forest; traffic sign classification; tree classifier; Histograms; Image color analysis; Image edge detection; Support vector machines; Training; Transforms; Vegetation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location
San Jose, CA
ISSN
2161-4393
Print_ISBN
978-1-4244-9635-8
Type
conf
DOI
10.1109/IJCNN.2011.6033494
Filename
6033494
Link To Document