Vehicle classification from traffic surveillance videos at a finer granularity

Authors:
Xin Chen;Chengcui Zhang
Affiliations:
Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL;Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL
Venue:
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Year:
2007

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Abstract

This paper explores the computer vision based vehicle classification problem at a fine granularity. A framework is presented which incorporates various aspects of an Intelligent Transportation System towards vehicle classification. Given a traffic video sequence, the proposed framework first segments individual vehicles. Then vehicle segments are processed so that all vehicles are along the same direction and measured at the same scale. A filtering algorithm is applied to smooth the vehicle segment image. After these three steps of preprocessing, an ICA based algorithms is implemented to identify the features of each vehicle type. One-class SVM is used to categorize each vehicle into a certain class. Experimental results show the effectiveness of the framework.