An anchor patch based optimisation framework for reducing optical flow drift in long image sequences

Wenbin Li, Darren Cosker, Matthew Brown

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

7 Citations (Scopus)
34 Downloads (Pure)

Abstract

Tracking through long image sequences is a fundamental research issue in computer vision. This task relies on estimating correspondences between image pairs over time where error accumulation in tracking can result in drift. In this paper, we propose an optimization framework that utilises a novel Anchor Patch algorithm which significantly reduces overall tracking errors given long sequences containing highly deformable objects. The framework may be applied to any tracking algorithm that calculates dense correspondences between images, e.g. optical flow. We demonstrate the success of our approach by showing significant tracking error reduction using 6 existing optical flow algorithms applied to a range of benchmark ground truth sequences. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.
Original languageEnglish
Title of host publicationComputer Vision – ACCV 2012
Subtitle of host publication11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III
EditorsKyoung Mu Lee, Yasuyuki Matsushita, James M Rehg, Zhanyi Hu
Place of PublicationBerlin
PublisherSpringer
Pages112-125
ISBN (Electronic)9783642374319
ISBN (Print)9783642374302
Publication statusPublished - 2013
Event11th Asian Conference on Computer Vision (ACCV) - , UK United Kingdom
Duration: 7 Nov 2012 → …

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7726
ISSN (Print)0302-9743

Conference

Conference11th Asian Conference on Computer Vision (ACCV)
CountryUK United Kingdom
Period7/11/12 → …

    Fingerprint

Cite this

Li, W., Cosker, D., & Brown, M. (2013). An anchor patch based optimisation framework for reducing optical flow drift in long image sequences. In K. M. Lee, Y. Matsushita, J. M. Rehg, & Z. Hu (Eds.), Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III (pp. 112-125). (Lecture Notes in Computer Science; Vol. 7726). Berlin: Springer.