Most of parametric motion segmentation methods, formulated based on RANSAC technique, are designed to estimate and segment multiple motions in a sequential manner. This paper introduces a new random set theoretical approach to simultaneously estimate the parameters of, and segment multiple motions in a single run. In this approach, the parameters of multiple motions are modelled as a random finite set with multi-Bernoulli distribution. Simulation results involving segmentation of numerous motions show that our method outperforms state-of-art methods in terms of estimation error and correct estimation rate. In addition, it is highly parallelizable and well-suited for implementation by parallel processors. The fast convergence and highly parallelizable nature of the proposed approach make it an excellent choice for real-time estimation and segmentation of multiple motions in computer vision and robotic applications.