Method for preceding vehicle type classification based on sparse representation

Yanwen Chong, Wu Chen, Zhilin Li, Hing Keung William Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)74-80
Number of pages7
JournalTransportation Research Record
Issue number2243
Publication statusPublished - 1 Dec 2011

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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