Detection of people in images

A. N. Rajagopalan, Philippe Burlina, Rama Chellappa

Research output: Contribution to conferencePaperpeer-review

Abstract

The paper describes a scheme for detecting and tracking people in images. The method effectively combines statistical information about the class of people with motion information for classification and tracking. In this scheme, the unknown distribution of the images of people is approximately modeled by learning higher order statistics (HOS) information of the `people class' from sample images. Given a test image, statistical information about the background is learnt dynamically. A motion detector identifies regions of activity in the image sequence. A classifier based on an HOS-based closeness measure then determines which of the moving objects actually correspond to people in motion. The tracking module uses position information and an HOS-based difference measurement vector to establish correspondence. When tested on real video data with a cluttered background, the performance of the method is found to be quite good. The method can also detect people in static imagery.

Original languageEnglish (US)
Pages2747-2752
Number of pages6
StatePublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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