Road Segmentation in Street View Images Using Texture Information

Road segmentation is a problem encountered fairly frequently, especially in the framework of scene understanding and self-driving cars. On the flip side, there are several Street View databases that offer large amounts of useful data, which are still relatively untapped. In this paper we propose a road segmentation algorithm specifically aimed at segmenting roads from street view images. We use fisher vectors to encode small windows extracted from the main image at multiple scales, then classify these patches and use a voting scheme to get the final segmentation. We optionally utilize a spatial prior and superpixels to improve our segmentation. Our algorithm performs well and outputs a good segmentation for further use in road evaluation. We test our method on the KITTI road dataset and compare it to the state of the art.

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