Method for Automatic Segmentation of Vehicles in Digital Images

Abstract

Introduction. Modern systems for active vehicle safety are designed to significantly reduce the number of road accidents. Sensors based on monocular cameras are increasingly being introduced by the world's leading automakers as an effective tool for improving traffic safety. Modern methods of localisation and classification, combined with semantic segmentation algorithms, allow for image division into independent groups of pixels corresponding to each object. However, the problem of developing segmentation algorithms ensuring improved quality of image segmentation remains to be solved.Aim. To develop an automatic method for segmenting a given object during image analysis.Materials and methods. An automatic method for segmenting vehicles in an image was proposed. The method presented herein allows semantic segmentation of the object of interest, based upon a priori information about the bounding boxes, which frame the objects in the image. Bounding box information is used to transform an image into a polar coordinate system where the pixels of the image act as the edges of a weighted graph. A closed contour is obtained around the object of interest by using the shortest path search algorithm and inverse transformation to the Cartesian coordinate system.Results. The experiments confirmed the correctness of the selected area of interest based on this algorithm. Jacquard’s similarity coefficient for the Carvana open database is 85 %. Furthermore, the proposed method was applied to different classes of images from the Pascal VOC database, thus demonstrating the ability to segment objects of other classes.Conclusion. The main contribution of the proposed method was as follows: 1) segmentation of the object of interest at the level of modern methods, and in some cases in excess thereof; 2) the study presents a new look at the way of tracking object contours.