@inproceedings{d67f635c4bb24c20b13c742635d13872,
title = "An anchor patch based optimisation framework for reducing optical flow drift in long image sequences",
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.",
author = "Wenbin Li and Darren Cosker and Matthew Brown",
year = "2013",
language = "English",
isbn = "9783642374302",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "112--125",
editor = "Lee, {Kyoung Mu} and Yasuyuki Matsushita and Rehg, {James M} and Zhanyi Hu",
booktitle = "Computer Vision – ACCV 2012",
note = "11th Asian Conference on Computer Vision (ACCV) ; Conference date: 07-11-2012",
}