Paper Info
Title
Tire Classification From Still Images And Video
Abstract
The use of different types of tires (e.g., all-season, snow, studded, summer) is regulated by law in several states and countries. Violation of tire usage laws typically results in substantial fines for infringers. In this paper, we propose an automated method to classify tires into snow, all-season and summer tires from still images or from a sequence of video frames. Our method first trains a Suport Vector Machine (SVM) classifier on features extracted from a set of training images. Classification of test tire images is a two-stage process that entails feature extraction and tire classification based on the processing of the extracted features by the previously trained SVM classifier. The principle underlying the feature extraction stage is the representation of tire images via a low-dimensional approximation obtained from Principal Component Analysis (PCA). In order to improve robustness to changes in illumination and perspective, the features are extracted from the frequency representation of the binary edge map of the tire tread image. Our experimental results show that the proposed method achieves high classification accuracy.
Year
DOI
Venue
2012
10.1109/ITSC.2012.6338693
2012 15TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Keywords
Field
DocType
tire classification, principal component analysis, support vector machine, frequency analysis, edge map, all-season, snow, studded, summer tires
Computer vision,Simulation,Edge detection,Tread,Support vector machine,Robustness (computer science),Feature extraction,Artificial intelligence,Engineering,Contextual image classification,Classifier (linguistics),Principal component analysis
Conference
ISSN
Citations 
PageRank 
2153-0009
1
0.36
References 
Authors
4
4
Name
Order
Citations
PageRank
Orhan Bulan112212.94
Edgar A. Bernal25810.32
Robert P. Loce314823.54
Wencheng Wu4507.92