Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18619
Title: Method for preceding vehicle type classification based on sparse representation
Authors: Chong, Y
Chen, W 
Li, Z 
Lam, WHK 
Issue Date: 2011
Publisher: U.S. National Research Council, Transportation Research Board
Source: Transportation research record : journal of the Transportation Research Board, 2011, no. 2243, p. 74-80 How to cite?
Journal: Transportation research record : journal of the Transportation Research Board 
Abstract: This paper proposes a novel vehicle-type classifier named SRCVT that uses video data collected from video detection units. The SRCVT uses the sparse representation classifier (SRC) technique without the requirement of an additional training procedure to construct the classification model. It classifies preceding vehicles directly from the testing samples' sparse representation, without the need for explicit model selection. The SRCVT consists of four steps: data preparation, principal component analysis transformation, realization, and classification output. The classifier has been compared with the traditional method of using a supported vector machine. The results show that the SRCVT is more promising for vehicle-type classification in terms of classification accuracy and ease of use.
URI: http://hdl.handle.net/10397/18619
ISSN: 0361-1981
DOI: 10.3141/2243-09
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