Fusing Geometry and Appearance for Road Segmentation

Gong Cheng, Yiming Qian, James H. Elder; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 166-173

Abstract


We propose a novel method for fusing geometric and appearance cues for road surface segmentation. Modeling colour cues using Gaussian mixtures allows the fusion to be performed optimally within a Bayesian framework, avoiding ad hoc weights. Adaptation to different scene conditions is accomplished through nearest-neighbour appearance model selection over a dictionary of mixture models learned from training data, and the thorny problem of selecting the number of components in each mixture is solved through a novel cross-validation approach. Quantitative evaluation reveals that the proposed fusion method significantly improves segmentation accuracy relative to a method that uses geometric cues alone.

Related Material


[pdf]
[bibtex]
@InProceedings{Cheng_2017_ICCV,
author = {Cheng, Gong and Qian, Yiming and Elder, James H.},
title = {Fusing Geometry and Appearance for Road Segmentation},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}