Traffic sign classification using K-d trees and Random Forests

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...

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Bibliographic Details
Main Authors: Zaklouta, F., Stanciulescu, B., Hamdoun, O.
Format: Conference Proceeding
Language:English
Subjects:
Histograms
Image color analysis
Image edge detection
Support vector machines
Training
Transforms
Vegetation
Online Access:Request full text
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Summary: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.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2011.6033494