Vehicle classification based on soft computing algorithms

Authors:
Piotr Dalka;Andrzej Czyżewski
Affiliations:
Gdansk University of Technology, Multimedia Systems Department, Gdansk, Poland;Gdansk University of Technology, Multimedia Systems Department, Gdansk, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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Abstract

Experiments and results regarding vehicle type classification are presented. Three classes of vehicles are recognized: sedans, vans and trucks. The system uses a non-calibrated traffic camera, therefore no direct vehicle dimensions are used. Various vehicle descriptors are tested, including those based on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images convolved with Gabor filters. Vehicle type is recognized with various classifiers: artificial neural network, K-nearest neighbors algorithm, decision tree and random forest.