A robust road segmentation method based on graph cut with learnable neighboring link weights | |
Jun Yuan1; Shuming Tang3; Fei Wang2; H. Zhang | |
2014 | |
会议日期 | 2014 |
会议地点 | Qingdao, China |
关键词 | Road Segmentation, learnable neighboring link weights, advanced driver assistance systems, monocular vision |
英文摘要 | 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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/41450] |
专题 | 自动化研究所_智能制造技术与系统研究中心 |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Chinese Academy of Sciences, Beijing, China 2.School of Information Engineering, Minzu University of China, Beijing, China 3.High-Tech Innovation Engineering Center, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Jun Yuan,Shuming Tang,Fei Wang,et al. A robust road segmentation method based on graph cut with learnable neighboring link weights[C]. 见:. Qingdao, China. 2014. |
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