Abstract: Motion computation is an important and challenging problem in the analysis of image sequences. Motion computation plays an important role in many applications such as target tracking and movement/change detection in surveillance systems; in compressing video images, if we have already compressed I (t-1), we know much about I (t). Typical approach for building a predicted image for I (t), based on I (t-1)�etc. There are numerous other applications. From a sequence of images we can only estimate an approximation of the image motion field called optical flow. Motion estimation and computation in images sequence is a difficult and computationally expensive task. The computation of optical flow is an ill-posed problem, which expresses itself as the aperture problem. However, motion vectors can be estimated by using differential methods, where optic flow estimation is based on computing spatial and temporal image derivatives. A typical way to overcome the ill-posed ness problems of differential optic flow methods consists of the use of smoothing techniques and smoothness assumption as a regularization methods, in which additional constraints functions are introduced. In this research we propose to improve optical flow estimation by including colour information as constraints functions in the optimization process. The proposed technique has shown encouraging results.