Optical measurement and motion estimation based on the acquired sequence of images is one of the most recent sensing techniques developed in the last decade or so. As a modern non-contact sensing technique, motion estimation and optical measurements provide a full-field awareness without any mass loading or change of stiffness in structures, which is unavoidable using other conventional transducers (e.g. accelerometers, strain gauges, and LVDTs). Among several motion estimation techniques prevalent in computer vision, phase-based motion estimation is one of the most reliable and accurate methods. However, contamination of the sequence of images with numerous sources of noise is inevitable, and the performance of the phase-based motion estimation could be affected due to the lighting changes, image acquisition noise, and the camera’s intrinsic sensor noise. Within this context, the uncertainty quantification (UQ) of the phase-based motion estimation (PME) has been investigated in this paper. Based on a normality assumption, a framework has been provided in order to characterize the propagation of the uncertainty from the acquired images to the estimated motion. The established analytical solution is validated via Monte-Carlo simulations using a set of simulation data. The UQ model in the paper is able to predict the order statistics of the noise influence, in which the uncertainty bounds of the estimated motion are given, after processing the contaminated sequence of images.