Abstract: The optical flow field is an approximation to the 2D motion field, which is produced by projecting the 3D scene velocity into the image. In order to be useful for tasks such as surface structure recovery or the estimation of viewer or object motion, the optical flow has to be dense and accurate. Optical flow computation is an important and challenging problem in the analysis of image sequences. It is a difficult and computationally expensive task and is an ill-posed problem, which expresses itself as the aperture problem. However, optical flow vectors can be estimated by using regularization methods, in which additional constraints functions are introduced. In this study we propose to improve optical flow estimation by including colour information as constraints functions in the optimization process. The proposed technique based an a simple matrix inversion using colour information as constraints functions in the optimization process and it has shown encouraging results.