In this paper we propose a new probabilistic relaxation framework to perform robust multiple motion estimation and segmentation from a sequence of images. Our approach uses displacement information obtained from tracked features or raw sparse optical flow to iteratively estimate multiple motion models. Each iteration consists of segmentation and a motion parameter estimation step. The motion models are used to compute probability density functions for all displacement vectors. Based on the estimated probabilities a pixel-wise segmentation decision is made by a Bayesian classifier, which is optimal in respect to minimum error. The updated segmentation then relaxes the motion parameter estimates. These two steps are iterated until the error of the fitted models is minimized. The Bayesian formulation provides a unified probabilistic framework for various motion models and induces inherent robustness through its rejection mechanism. An implementation of the proposed framework using translational and affine motion models is presented. Its superior performance on real image sequences containing multiple and fragmented motions is demonstrated.