A simplified error model for height estimation using a single camera

P. Viswanath, I. A. Kakadiaris, S. K. Shah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Intelligent solutions for video surveillance require reliable object features for robust tracking. Surveillance cameras are generally low resolution and hence impose restrictions on the ability to identify sufficient features and descriptors for effective tracking. In such applications, reliable estimate of the object height from a single uncalibrated camera can prove to be a very useful feature. Several approaches have been developed to obtain height information of an object from image sequences, but are limited in their utility due to errors arising from varying image and object properties. In this paper, we present a simplified error model dependent on the location of the object in the image and the estimated camera height. Further, the developed model is used to obtain reliable height estimates of objects in different image sequences and the result is compared to estimates obtained without the use of the model. Significant reduction in error associated with the measurement is observed while making the estimate independent of camera and object variations. We then demonstrate its utility in object tracking.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages1259-1266
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: Sep 27 2009Oct 4 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Country/TerritoryJapan
CityKyoto
Period9/27/0910/4/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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