Monocular vision-based drivable region labeling using adaptive region growing

The ability of intelligent vehicles to determine drivable region and perceive obstacles in dynamic environments is essential for maintaining safety and preventing accidents. In this paper, a vision-based drivable region labeling method is proposed. The method is based on an adaptive growing-based approach combining color features restrictions from an indicated drivable region in an efficient, stable, and precise method that can work in various scenes. The proposed method demonstrates that it distinguishes robustly and precisely between drivable region and non-drivable region in freeway, urban, rural road scenes with illuminant variance conditions, using color features restrictions estimated from indicated drivable region without specific machine learning algorithms.