Vision-based vehicle classification

Surendra Gupte, Osama Masoud, Nikolaos P. Papanikolopoulos

Research output: Contribution to conferencePaperpeer-review

33 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, blob level and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. Kalman filtering is used to estimate vehicle parameters. The proposed method is based on the establishment of correspondences among blobs and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method.

Original languageEnglish (US)
Number of pages6
StatePublished - Jan 1 2000
Event2000 IEEE Intelligent Transportation Systems Proceedings - Dearborn, MI, USA
Duration: Oct 1 2000Oct 3 2000


Conference2000 IEEE Intelligent Transportation Systems Proceedings
CityDearborn, MI, USA


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