Search for identical points in the inter-pixel space of video images

Abstract

One of the ways to describe objects in images is to identify some of their characteristic points or points of attention. Areas surrounding attention points are described by descriptors (a set of features) in such a way that they can be identified and compared. On these features the search for identical points on other images is carried out by scanning them with a sliding window. The most famous descriptors and methods for finding identical points are: SIFT, SURF, GLOH, BRIEF and others. This group of methods is characterized by the fact that the displacement of identical points in video images can be arbitrary, but the accuracy of calculating their coordinates depends on the bit grid of video images and, in the best case, is equal to the interpixel distance. Another group of methods that can be used to track identical points of video images are methods built on the basis of optical flow calculation. One of the popular methods of tracking points based on optical flow calculation is the Lucas-Kanade method. It allows you to calculate the displacement of points in the interpixel space due to the solution of differential equations. To date, the Lucas-Kanade method has several modifications. A limitation of these methods is that the neighborhoods of the shifted points must overlap to a large extent. The article investigates and proposes the complex application of methods of scanning video images with a sliding window and differential calculation of optical flow, which allows to increase the accuracy and speed of calculating the coordinates of identical points in the images in relation to the search for these points only by scanning. A more accurate calculation of the coordinates of the characteristic points of the object in the interpixel space of video images will lead to a more accurate determination of the position and orientation of these objects in 3D space. The simulation was carried out using the method of rough search for identical points of video images described by invariant moments and specifying their coordinates using the Lucas-Kanade point tracking method. The simulation results indicate an increase in speed by almost an order of magnitude and, according to indirect estimates, the accuracy of calculations.