A method of motion segmentation in RGB image sequences is presented in details. The method is based on moving object modeling by a six-variate Gaussian distribution and a hidden Markov random field (MRF) framework. It is an extended and improved version of our previous work. Based on mathematical principles the energy expression of MRF is modified. Moreover, an initialization procedure for the first frame of the sequence is introduced. Both modifications result in new interesting features. The first involves a rather simple parameter estimation which has to be performed before the use of the method. Now, the values of Maximum Likelihood (ML) estimators of the parameters can be used without any user's modifications. The last allows one to avoid finding manually the localization mask of moving object in the first frame. Experimental results showing the usefulness of the method are also included.