Video-based road detection via online structural learning

被引：39

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; LANE DETECTION; TRACKING; VISION; 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

页码：336 / 347

页数：12

共 50 条

[1] Video-Based Learning and Open Online Courses
Giannakos, Michail N.
Chorianopoulos, Konstantinos
Ronchetti, Marco
Szegedi, Peter
Teasley, Stephanie D.
[J]. INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2014, 9 (01): : 4 - 7
[2] Epistemic Emotion Detection by Video-based and Heart Rate Variability Features for Online Learning
Bounyong, Souksakhone
Yoshida, Ryuga
Yoshioka, Mototaka
[J]. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2023, 2023-January
[3] Mind wandering state detection during video-based learning via EEG
Tang, Shaohua
Liang, Yutong
Li, Zheng
[J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2023, 17
[4] Video-Based Abnormal Driving Behavior Detection via Deep Learning Fusions
Huang, Wei
Liu, Xi
Luo, Mingyuan
Zhang, Peng
Wang, Wei
Wang, Jin
[J]. IEEE ACCESS, 2019, 7 : 64571 - 64582
[5] Epistemic Emotion Detection by Video-based and Heart Rate Variability Features for Online Learning
Bounyong, Souksakhone
Yoshida, Ryuga
Yoshioka, Mototaka
[J]. 2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE, 2023,
[6] A Novel Video-based Application for Road Markings Detection and Recognition
Tang, Zongzhi
Boukerche, Azzedine
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
[7] VIDEO-BASED VEHICLE DETECTION ON A TWO-WAY ROAD
Czapla, Zbigniew
[J]. SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT, 2016, 92 : 23 - 29
[8] Online appearance model learning for video-based face recognition
Liu, Liang
Wang, Yunhong
Tan, Tieniu
[J]. 2007 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-8, 2007, : 2912 - +
[9] Video-based Online Learning: The Other Side of the Looking Glass
Kawano, Daniel Takashi
Tongue, Benson H.
[J]. 2014 ASEE ANNUAL CONFERENCE, 2014,
[10] Video-Based Stress Detection through Deep Learning
Zhang, Huijun
Feng, Ling
Li, Ningyun
Jin, Zhanyu
Cao, Lei
[J]. SENSORS, 2020, 20 (19) : 1 - 17

← 1 2 3 4 5 →