Motion estimation is the process of determining motion vectors that describe the transformation from one 2D image to another; typically from frame to frame in video. It’s more general than simple motion detection as it seeks to quantify how each part of the image has moved. Optical flow algorithms are a primary example, estimating a flow vector (velocity) for each pixel or feature point between two frames. This is useful for video compression (MPEG uses block-based motion estimation to encode differences), video stabilization (estimating how the camera moved to correct it), and robot navigation or depth from motion (structure from motion). Methods range from block matching (dividing frame into blocks and finding the best match in the next frame) to differential methods like Lucas-Kanade or Horn-Schunck optical flow, and even deep learning approaches for optical flow estimation (e.g., FlowNet). Accurate motion estimation is challenging due to occlusions, illumination changes, and the aperture problem (local motion can be ambiguous).