Figure 4. Anchor patches (blue patches) are labeled on non-anchor frames within every clip using SIFT feature matching and Barycentric Coordinate Mapping between reference frame and non-anchor frame.

Tracking nonrigid surface 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 thesis, I propose an optimisation framework with a novel Anchor Patch based algorithm which significantly reduces overall tracking errors given long sequences containing nonrigidly deformable objects. The framework may be applied to any tracking algorithm that calculates dense correspondences between images, e.g. optical flow. This work demonstrates the success of the proposed approach by showing significant tracking error reduction using 6 existing optical flow algorithms applied to a range of nonrigid benchmarks. This work also provides quantitative analysis of this approach given synthetic occlusions and image noise.

Related Papers:

W. Li, D. Cosker, and M. Brown, Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences, Journal of Intelligent and Fuzzy Systems (JIFS'16), 2016. [PDF]

W. Li, D. Cosker, and M. Brown, An Anchor Patch Based Optimisation Framework for Reducing Optical Flow Drift in Long Image Sequences, in Proceeding of Asian Conference on Computer Vision (ACCV’12), Springer, November 2012, pp. 112–125. [PDF]