Video-based road detection via online structural learning

被引：43
作者：
Yuan, Yuan [1 ]
Jiang, Zhiyu [1 ,2 ]
Wang, Qi [3 ,4 ]
机构：
[1] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
[4] Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 168卷
基金：
中国国家自然科学基金;
关键词：
Computer vision; Machine learning; Road detection; Structural SVM; Online updating; Road boundary; VISION; TRACKING; CLASSIFICATION; SALIENCY; SYSTEM; SCALE;
D O I：
10.1016/j.neucom.2015.05.092
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Video-based road detection is a crucial enabler for the successful development of driver assistant and robot navigation systems. But reliable detection is still on its infancy and deserves further research. In order to adapt to the situation consisting of environmental varieties, an online framework is proposed focusing on exploring the structure cue of the feature vectors. Through the structural support vector machine, the road boundary and non-boundary instances are firstly discriminated. Then they are utilized to fit a complete road boundary. After that, the road region is accordingly inferred and the obtained results are treated as ground truth to update the learned model. Three contributions are claimed in this work: online-learning updating, structural information consideration, and targeted sampling selection. The proposed method is finally evaluated on several challenging videos captured by ourselves. Qualitative and quantitative results show that it outperforms the other competitors. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：336 / 347
页数：12
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