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.
|Title of host publication||Computer Vision – ACCV 2012|
|Subtitle of host publication||11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III|
|Editors||Kyoung Mu Lee, Yasuyuki Matsushita, James M Rehg, Zhanyi Hu|
|Place of Publication||Berlin|
|Publication status||Published - 2013|
|Event||11th Asian Conference on Computer Vision (ACCV) - , UK United Kingdom|
Duration: 7 Nov 2012 → …
|Name||Lecture Notes in Computer Science|
|Conference||11th Asian Conference on Computer Vision (ACCV)|
|Country||UK United Kingdom|
|Period||7/11/12 → …|
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.