Paper Info
Title
Robust road detection from a single image using road shape prior
Abstract
Many road detection algorithms require pre-learned information, which may be unreliable as the road scene is usually unexpectable. Single image based (i.e., without any pre-learned information) road detection techniques can be adopted to overcome this problem, while their robustness needs improving. To achieve robust road detection from a single image, this paper proposes a general road shape prior to enforce the detected region to be road-shaped by encoding the prior into a graph-cut segmentation framework, where the training data is automatically generated from a predicted road region of the current image. By iteratively performing the graph-cut segmentation, an accurate road region will be obtained. Quantitative and qualitative experiments on the challenging SUN Database validate the robustness and efficiency of our method. We believe that the road shape prior can also be used to yield improvements for many other road detection algorithms.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738568
ICIP
Keywords
Field
DocType
graph cuts,sun database,visual databases,image segmentation,roads,road detection,object detection,single image,robust road detection,shape prior,prelearned information,graph theory,road shape prior,training data,graph-cut segmentation framework
Graph theory,Training set,Computer vision,Object detection,Pattern recognition,Computer science,Segmentation,Image based,Image segmentation,Robustness (computer science),Artificial intelligence,Encoding (memory)
Conference
ISSN
Citations 
PageRank 
1522-4880
14
0.77
References 
Authors
7
4
Name
Order
Citations
PageRank
Zhen He1203.56
Tao Wu25811.53
Zhipeng Xiao3322.48
Hangen He430723.86