Optical Flow with Semantic Segmentation and Localized Layers
This addresses the challenge of accurate optical flow estimation in complex scenes for computer vision applications, representing a significant but incremental improvement over existing methods.
The paper tackled the problem of optical flow estimation by incorporating semantic segmentation to model object-specific motion, achieving the lowest error among published monocular methods on the KITTI-2015 benchmark.
Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, we model the motion on roads with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine motion plus deviations. We then pose the flow estimation problem using a novel formulation of localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our semantic flow method achieves the lowest error of any published monocular method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos.