Stereo-image-based ground-line prediction and obstacle detection

In recent years, vision systems have become essential in the development of advanced driver assistance systems or autonomous vehicles. Although deep learning methods have been the center of focus in recent years to develop fast and reliable obstacle detection solutions, they face difficulties in complex and unknown environments where objects of varying types and shapes are present. In this study, a novel non-AI approach is presented for finding the ground-line and detecting the obstacles in roads using v-disparity data. The main motivation behind the study is that the ground-line estimation errors cause greater deviations at the output. Hence, a novel ground plane is defined as a region in the v-disparity map by using random variables to minimize these errors. In this new approach, weighted least squares regression, outlier detection, and camera height approximation were utilized for determining the ground region with higher accuracy. KITTY-2 dataset was chosen to conduct validation and evaluation experiments of the proposed approach. The experiment results were presented in GitHub, and the performance comparison shows that the proposed approach provides at least 20% improvement over Hough transform, which is a widely used non-AI algorithm. The results were also compared with a recently published article data and the best outcome was obtained among them for the recall metric.