Detection and Classification of Vehicles

Surendra Gupte, Osama Masoud, Robert F.K. Martin, Nikolaos P Papanikolopoulos

Research output: Contribution to journalArticle

638 Scopus citations


This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done, at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.

Original languageEnglish (US)
Pages (from-to)37-47
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number1
StatePublished - Mar 1 2002


  • Camera calibration
  • Vehicle classification
  • Vehicle detection
  • Vehicle tracking

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