Checkerboard-type filtering for a low-power gradient-based optical flow estimation system

In this paper, we propose a checkerboard-type filtering algorithm for a low-power gradient-based optical flow estimation (OFE) system. A gradient-based optical flow algorithm estimates optical flow field using gradient values of images. The conventional filtering stages in OFE that are used to calculate gradient values are composed of a 3D prefiltering/smoothing stage and three 1D parallel derivative filtering stages. To reduce power consumption using pixel-wise parallel processing in analog spatial transform imager, we suggest a checkerboard-type filtering based on convolution theorem and common data sharing. We describe the equivalence of our filtering scheme with the conventional smoothing and derivative filtering schemes and present some comparison in terms of the number of operations and power savings. We show that our approach is promising for low-power implementation of gradient-based OFE system under proper data scanning direction.