Paper Info
Title
Night-Time Road Boundary Detection with Infrared Channel Features Classifier
Abstract
Road boundary detection is very important to Intelligent Vehicle (IV) System. Recently, road boundary detection during night-time driving condition attracts more and more attentions. In this paper, we propose a novel and fast method for night-time road boundary detection on infrared images. Firstly, a set of novel Infrared Channel Features (ICF) are proposed for describing infrared image patterns. Furthermore, we proposed an Infrared Edge classifier to generate a task-driven probability edge map. Finally, road boundary extraction is performed on the edge map by two steps: searching available road boundaries and second order polynomial approximation. Experiment show that the proposed method performs well with effectiveness and efficiency.
Year
DOI
Venue
2014
10.1109/CIT.2014.35
CIT
Keywords
Field
DocType
icf,infrared channel features classifier,road vehicles,infrared edge classifier,intelligent vehicle system,infrared channel features,night vision,night-time driving condition,iv system,random forests,curve fitting,polynomial approximation,road boundary extraction,infrared channel features, road boundary detection, random forests, curve fitting,night-time road boundary detection,feature extraction,image classification,infrared image patterns,second order polynomial approximation,task-driven probability edge map,object detection,road boundary detection,road traffic,intelligent transportation systems,probability
Computer vision,Infrared image,Polynomial,Curve fitting,Computer science,Communication channel,Boundary detection,Artificial intelligence,Classifier (linguistics),Random forest,Infrared
Conference
ISSN
Citations 
PageRank 
2474-9648
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Jing Dai100.34
Yuqiang Fang21288.93
Tao Wu35811.53
Dawei Zhao452.43
Hangen He530723.86