Vehicle type classification from visual-based dimension estimation

A. H.S. Lai, G. S.K. Fung, N. H.C. Yung

Research output: Contribution to conferencePaperpeer-review

102 Scopus citations


This paper presents a visual-based dimension estimation method for vehicle type classification. Our method extracts moving vehicles from traffic image sequences and fits them with a simple deformable vehicle model. Using a set of coordination mapping functions derived from a calibrated camera model and relying on a shadow removal method, vehicle's width, length and height are estimated. Our experimental tests show that the modeling method is effective and the estimation accuracy is sufficient for general vehicle type classification.

Original languageEnglish (US)
Number of pages6
StatePublished - Jan 1 2001
Event2001 IEEE Intelligent Transportation Systems Proceedings - Oakland, CA, United States
Duration: Aug 25 2001Aug 29 2001


Other2001 IEEE Intelligent Transportation Systems Proceedings
Country/TerritoryUnited States
CityOakland, CA


  • Camera calibration
  • Dimension estimation
  • Shadow removal
  • Vehicle modeling

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


Dive into the research topics of 'Vehicle type classification from visual-based dimension estimation'. Together they form a unique fingerprint.

Cite this