Detection and Classification of Vehicles

Gupte, S; Masoud, O; Martin, RFK; Papanikolopoulos, NP

dc.contributor.author	Gupte, S	en
dc.contributor.author	Masoud, O	en
dc.contributor.author	Martin, RFK	en
dc.contributor.author	Papanikolopoulos, NP	en
dc.date.accessioned	2014-03-01T01:51:51Z
dc.date.available	2014-03-01T01:51:51Z
dc.date.issued	2002	en
dc.identifier.issn	15249050	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/26481
dc.subject	Camera calibration	en
dc.subject	Vehicle classification	en
dc.subject	Vehicle detection	en
dc.subject	Vehicle tracking	en
dc.title	Detection and Classification of Vehicles	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/6979.994794	en
heal.identifier.secondary	http://dx.doi.org/10.1109/6979.994794	en
heal.publicationDate	2002	en
heal.abstract	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.	en
heal.journalName	IEEE Transactions on Intelligent Transportation Systems	en
dc.identifier.doi	10.1109/6979.994794	en
dc.identifier.volume	3	en
dc.identifier.issue	1	en
dc.identifier.spage	37	en
dc.identifier.epage	47	en