RvGIST: A Holistic Road Feature for Real-Time Road-Scene Understanding

摘要

Image-based road scene understanding is a critical issue for intelligent vehicles and autonomous mobile robots. It is challenging to deal with varying road conditions in a dynamic environment in real time. This paper presents an effective while simple approach to classify road types and locate the road-related elements through the analysis of a holistic visual road feature. The feature is abstracted from responses of Gabor-filter-set and grouped into super-pixel grids, consisting of road-scene textural context and dominant orientation distribution. From this feature we successively deduce the information of horizon line, road type and coarse locations of road surface, lanes, on-road obstacles and off-road regions. Experiments show that the proposed analyzing method based on the new holistic feature is beneficial to road estimation and vehicle detection in complex road scenes, achieving improvements in both accuracy and efficiency.