@inproceedings{1c915ee3ec3844898907bf94b0b898d6,
title = "Adaptive road detection towards multiscale-multilevel probabilistic analysis",
abstract = "Vision-based road detection is a challenging problem because of the changeable shape and varying illumination. Though many efforts have been spent on this topic, the achieved performance is far from satisfactory. To this end, this paper formulates a Bayesian method which simultaneously explores the multiscale-multilevel clues that are considered to be complementary. Two contributions are claimed in this proposed method. 1) By computing the prior distribution in superpixellevel with a novel Laplacian Sparse Subspace Clustering and observation likelihood in pixel-level with statistical color similarity, the posterior probability of road region can be effectively inferred. 2) To ensure the adaptivity of road model in various conditions, a multiscale strategy is presented to fuse the detection results of different scales. Experimental results on several challenging video sequences verify the superiority of the proposed method compared with several popular ones.",
keywords = "Bayesian, clustering, Computer vision, road detection, sparse, superpixel",
author = "Zhiyu Jiang and Qi Wang and Yuan Yuan",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 ; Conference date: 09-07-2014 Through 13-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/ChinaSIP.2014.6889334",
language = "英语",
series = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "698--702",
booktitle = "2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings",
}