The Effect of Presmoothing Image Sequences on the Computation of Optical Flow

Abstract

AbstractThe computation of optical flow has been proposed as a pre-processing step for many high-level vision algorithms. One of the main approaches to the optical flow problem is the gradient-based approach which differentiates the image intensity to compute the optical flow. Often motion vectors computed using various approaches are not reliable. Spatial smoothing of the image sequence is advisable in order to improve velocity estimates in the presence of noise. That is, the application of some kind of linear operation to the images in the sequence before solving for the flow. The pre-processing typically takes the form of some type of spatial Gaussian smoothing or scale specific band-pass filtering of the input images. The main objectives of such filtering have been to lessen the effects of noise, to isolate image structure of interest and to attenuate temporal aliasing and quantization effects in the input images.This paper investigates the effect of presmoothing on this computation of optical flow. The well known method of Horn and Schunck as well as our own finite element method are implemented and tested for improvements due to presmoothing. Discussions are provided on the effects of presmoothing for a variety of image sequences. In our experiments, smoothing is carried out on a selection of well known sequences in both time and space. Improvements are shown using presmoothing.KeywordsFinite Volume MethodsInverse Finite Element MethodsMotion TrackingOptical FlowSpatial SmoothingTemporal Smoothing