Using color to compute optical flow

In order to study the motion of non-rigid objects in an image sequence, it is often necessary to compute a dense optical flow field. Gradient-based techniques have been relatively successful at computing dense flow fields for gray-scale images when additional constraints are added. However, these constraints often introduce errors at the edges of objects, where the object motion is different from the background motion. Our approach is to use color images to obtain three constraint equations corresponding to three color components. This gives us an overdetermined linear system with three equations and two unknowns (x and y direction components of optical flow) which can be solved using linear least-squares algorithm. No further constraints are necessary. This paper presents such a color-based optical flow estimation and includes some initial promising results on synthetic motion image data. Further research directions are also discussed.