Visual object tracking using adaptive correlation filters

David S. Bolme, J. Ross Beveridge, Bruce A. Draper, Yui Man Lui

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

3398 Scopus citations

Abstract

Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of current state-of-the-art techniques. The oldest and simplest correlation filters use simple templates and generally fail when applied to tracking. More modern approaches such as ASEF and UMACE perform better, but their training needs are poorly suited to tracking. Visual tracking requires robust filters to be trained from a single frame and dynamically adapted as the appearance of the target object changes. This paper presents a new type of correlation filter, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame. A tracker based upon MOSSE filters is robust to variations in lighting, scale, pose, and nonrigid deformations while operating at 669 frames per second. Occlusion is detected based upon the peak-to-sidelobe ratio, which enables the tracker to pause and resume where it left off when the object reappears.

Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2544-2550
Number of pages7
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 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
Period06/13/1006/18/10

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