This paper addresses the problem of robust 2D image motion estimation in natural environments. We develop an adaptive tracking-region selection and optical-flow estimation technique. The strategy of adaptive region selection locates reliable tracking regions and makes their motion estimation more reliable and computationally efficient. The multi-stage estimation procedure makes it possible to discriminate between good and poor estimation areas, which maximizes the quality of the final motion estimation. Furthermore, the model fitting stage further reduces the estimation error and provides a more compact and flexible motion field representation that is better suited for high-level vision processing. We demonstrate the performance of our techniques on both synthetic and natural image sequences.