Motion estimation methods and noisy phenomena

Abstract

In an infrared surveillance system (which must detect remote sources and thus has a very low resolution) in an aerospace environment, the estimation of the cloudy sky velocity should lower the false alarm rate in discriminating the motion between various moving shapes by means of a background velocity map. The optical flow constraint equation, based on a Taylor expansion of the intensity function, is often used to estimate the motion for each pixel. One of the main problems in motion estimation is that, for one pixel, the real velocity cannot be found because of the aperture problem. Another kinematic estimation method is based on a matched filter [generalized Hough transform (GHT)]: it gives a global velocity estimation for a set of pixels. On the one hand we obtain a local velocity estimation for each pixel with little credibility because the optical flow is so sensitivity to noise; on the other hand, we obtain a robust global kinematic estimation, the same for all selected pixels. This paper aims to adapt and improve the GHT in our typical application in which one must discern the global movement of objects (clouds), whatever their form may be (clouds with hazy edges or distorted shapes or even clouds that have very little structure). We propose an improvement of the GHT algorithm by segmentation images with polar constraints on spatial gradients. One pixel, at timet, is matched with another one at timet + ΔT, only if the direction and modulus of the gradient are similar. This technique, which is very efficient, sharpens the peak and improves the motion resolution. Each of these estimations is calculated within windows belonging to the image, these windows being selected by means of an entropy criterion. The kinematic vector is computed accurately by means of the optical flow constraint equation applied on the displaced window. We showed that, for small displacements, the optical flow constraint equation sharpens the results of the GHT. Thus a semi-dense velocity field is obtained for cloud edges. A velocity map computed on real sequences with these methods is shown. In this way, a kinematic parameter discriminates between a target and the cloudy background.