Extending the stixel world with online self-supervised color modeling for road-versus-obstacle segmentation

Abstract

This work concentrates on vision processing for Advanced Driver Assistance Systems (ADAS) and intelligent vehicle applications. The authors propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. The authors extension learns color appearance models for road and obstacle classes in an online and self-supervised fashion. The algorithm is tightly integrated within the core of the optimization process of the original Stixel World, allowing for strong fusion of the disparity and color signals. The authors perform an extensive evaluation, including different self-supervised learning strategies and different color models. The authors newly recorded, publicly available data set is intentionally focused on challenging traffic scenes with many low-texture regions, causing numerous disparity artifacts. In this evaluation, the authors increase the F-score of the drivable distance from 0.86 to 0.97, compared to a tuned version of the state-of-the-art baseline method. This clearly shows that the authors color extension increases the robustness of the Stixel World, by reducing the number of falsely detected obstacles while not deteriorating the detection of true obstacles.