Rapid selection of reliable templates for visual tracking

Nicolas Alt, Stefan Hinterstoisser, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

47 Scopus citations

Abstract

We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector regressor is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speed up the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.

Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages1355-1362
Number of pages8
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: 13 Jun 201018 Jun 2010

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period13/06/1018/06/10

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