TY - GEN
T1 - A robust road segmentation method based on graph cut with learnable neighboring link weights
AU - Yuan, Jun
AU - Tang, Shuming
AU - Wang, Fei
AU - Zhang, Hong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/14
Y1 - 2014/11/14
N2 - Road region detection is a crucial functionality for road following in advanced driver assistance systems (ADAS). To address the problem of environment interference in road segmentation through a monocular vision approach, a novel graph-cut based method is proposed in this paper. The novelty of this proposal is that weights of neighboring links (n-links) in a s-t graph are estimated by Multilayer Perceptrons (MLPs) rather than calculating by the neighboring contrast simply in previous graph-cut based methods. Estimating n-link weights by MLPs reinforces the ability of graph-cut based road segmentation algorithms to tolerate the complex and changeable appearance of road surfaces. Additionally, the Gentle AdaBoost algorithm is integrated into the graph-cut framework to estimate the terminal link (t-link) weights in the s-t graph. Experiments are conducted to show the robustness and efficiency of the proposed method.
AB - Road region detection is a crucial functionality for road following in advanced driver assistance systems (ADAS). To address the problem of environment interference in road segmentation through a monocular vision approach, a novel graph-cut based method is proposed in this paper. The novelty of this proposal is that weights of neighboring links (n-links) in a s-t graph are estimated by Multilayer Perceptrons (MLPs) rather than calculating by the neighboring contrast simply in previous graph-cut based methods. Estimating n-link weights by MLPs reinforces the ability of graph-cut based road segmentation algorithms to tolerate the complex and changeable appearance of road surfaces. Additionally, the Gentle AdaBoost algorithm is integrated into the graph-cut framework to estimate the terminal link (t-link) weights in the s-t graph. Experiments are conducted to show the robustness and efficiency of the proposed method.
UR - https://www.scopus.com/pages/publications/84937127488
U2 - 10.1109/ITSC.2014.6957929
DO - 10.1109/ITSC.2014.6957929
M3 - 会议稿件
AN - SCOPUS:84937127488
T3 - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
SP - 1644
EP - 1649
BT - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014
Y2 - 8 October 2014 through 11 October 2014
ER -