Optical flow generation in color images with using color derivative vector

The paper proposes a novel method for estimating the optical flows from the sequentially captured images with using their own color information. The gradient method is well known as one of the conventional methods to estimate the flows, and then the spatial and temporal derivative of the images are used in the method. Since the color images have richer information than the monochrome ones, they should contribute for estimating the more precise optical flows. In our approach, the color derivative vector (CDV) is introduced to bring out the information in the color images for the optical flow estimation. The CDV is derived from the derivatives of color images, and the optimal CDV provides the concrete weighting values of the RGB data. The optimal CDV is obtained with using the eigenvalues and the eigenvectors of the matrix consisting of the spatial and temporal color derivatives.