This paper presents a novel method for semantic segmentation and object recognition in a road scene using a hierarchical bag-of-textons method. Current driving-assistance systems rely on multiple vehicle-mounted cameras to perceive the road environment. The proposed method relies on integrated color and near-infrared images and uses the hierarchical bag-of-textons method to recognize the spatial configuration of objects and extract contextual information from the background. The histogram of the hierarchical bag-of-textons is concatenated to textons extracted from a multiscale grid window to automatically learn the spatial context for semantic segmentation. Experimental results show that the proposed method has better segmentation accuracy than the conventional bag-of-textons method. By integrating it with other scene interpretation systems, the proposed system can be used to understand road scenes for vehicle environment perception.
Multiband Image Segmentation and Object Recognition for Understanding Road Scenes
IEEE Transactions on Intelligent Transportation Systems ; 12 , 4 ; 1423-1433
2011-12-01
1848468 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multiband Image Segmentation and Object Recognition for Understanding Road Scenes
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