An Anchor Patch Based Optimization Framework for Reducing Optical Flow Drift in Long Image Sequences

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.