Higher order statistical learning for vehicle detection in images

A. N. Rajagopalan, Philippe Burlina, Rama Chellappa

Research output: Contribution to conferencePaper

Abstract

The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order statistics (HOS) information of the `vehicle class' from sample images. Given a test image, statistical information about the background is learnt `on the fly'. An HOS-based decision measure then classifies test patterns as vehicles or otherwise. When tested on real images of aerial views of vehicular activity, the method gives good results even on complicated scenes. It does not require any a priori information about the site. However, it is amenable to augmentation with contextual information. The method can serve as an important step towards building an automated roadway monitoring system.

Original languageEnglish (US)
Pages1204-1209
Number of pages6
DOIs
StatePublished - 1999
EventProceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) - Kerkyra, Greece
Duration: Sep 20 1999Sep 27 1999

Other

OtherProceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99)
CityKerkyra, Greece
Period9/20/999/27/99

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Rajagopalan, A. N., Burlina, P., & Chellappa, R. (1999). Higher order statistical learning for vehicle detection in images. 1204-1209. Paper presented at Proceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99), Kerkyra, Greece, . https://doi.org/10.1109/iccv.1999.790417