Optical flow computation may be divided into four processing steps where the first is extraction of image features suitable for flow estimation. Using a generalization of the basic flow constraint it is possible to estimate flow vectors from a set of feature images obtained from the input image sequence. Generalized Sobel operators provide a suitable set of feature extraction operators. The set of five filters up to second order provides good approximations of ideal edge and line detectors. A review of spatial and frequency constraints on optical flow suggests a multiresolution approach to optical flow where the initial feature extractors consists of velocity tuned operators. We show that two-dimensional generalized Sobel operators may be extended to spatio-temporal velocity tuned filters for optical flow estimation. Experimental results compare well to existing methods.
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