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Road environment modeling using robust perspective analysis and recursive Bayesian segmentation
Nieto Doncel, Marcos and Arróspide Laborda, Jon and Salgado Álvarez de Sotomayor, Luis
Road environment modeling using robust perspective analysis and recursive Bayesian segmentation.
"Machine Vision And Applications", v. 22
Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this ﬁeld of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectiﬁed image of the road plane. This rectiﬁed plane is used by a recursive Bayesian classi- ﬁer, which classiﬁes pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.
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