Motion Estimation with Optical Flow

The problem of optical flow may be expressed as:

where between consecutive frames, we can express the image intensity \((I)\) as a function of space \((x, y)\) and time \((t)\). Sparse optical flow gives the flow vectors of some “interesting features” (say few pixels depicting the edges or corners of an object) within the frame while Dense optical flow, which gives the flow vectors of the entire frame (all pixels) – up to one flow vector per pixel. Sparse optical flow selects a sparse feature set of pixels (e.g. interesting features such as edges and corners) to track its velocity vectors (motion).

Source: blog.nanonets.com