Computer vision in roadway transportation systems: a survey

被引:32
作者
Loce, Robert P. [1 ]
Bernal, Edgar A. [1 ]
Wu, Wencheng [1 ]
Bala, Raja [1 ]
机构
[1] Xerox Corp, Xerox Res Ctr Webster, Webster, NY 14580 USA
关键词
CAMERA CALIBRATION; MACHINE VISION; LANE DETECTION; PEDESTRIAN DETECTION; SIGN RECOGNITION; PHOTO-RADAR; VIDEO; VEHICLE; SPEED; ENFORCEMENT;
D O I
10.1117/1.JEI.22.4.041121
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
There is a worldwide effort to apply 21st century intelligence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emission reduction. Computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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页数:24
相关论文
共 199 条
[71]   The networked photo-enforcement and traffic monitoring system unicam [J].
Fucík, O ;
Zemcík, P ;
Tupec, P ;
Crha, L ;
Herout, A .
11TH IEEE INTERNATIONAL CONFERENCE AND WORKSHOP ON THE ENGINEERING OF COMPUTER-BASED SYSTEMS, PROCEEDINGS, 2004, :423-428
[72]   Recognition of traffic signs based on their colour and shape features extracted using human vision models [J].
Gao, X. W. ;
Podladchikova, L. ;
Shaposhnikov, D. ;
Hong, K. ;
Shevtsova, N. .
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2006, 17 (04) :675-685
[73]  
Garg R., 2005, ANAL VEHICLE RECOGNI
[74]   The effects of speed enforcement with mobile radar on speed and accidents - An evaluation study on rural roads in the Dutch province Friesland [J].
Goldenbeld, C ;
van Schagen, I .
ACCIDENT ANALYSIS AND PREVENTION, 2005, 37 (06) :1135-1144
[75]   Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition [J].
Gomez-Moreno, Hilario ;
Maldonado-Bascon, Saturnino ;
Gil-Jimenez, Pedro ;
Lafuente-Arroyo, Sergio .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2010, 11 (04) :917-930
[76]  
Grammatikopoulos L., 2005, INT S MOD TECHN ED P, P3
[77]   Parametric model of the perspective projection of a road with applications to lane keeping and 3D road reconstruction [J].
Guiducci, A .
COMPUTER VISION AND IMAGE UNDERSTANDING, 1999, 73 (03) :414-427
[78]   In the Eye of the Beholder: A Survey of Models for Eyes and Gaze [J].
Hansen, Dan Witzner ;
Ji, Qiang .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2010, 32 (03) :478-500
[79]  
Hashimoto N., 1990, SUMITOMO ELECT TECH, V25, P133
[80]  
Heisele B, 1998, INT C PATT RECOG, P1325, DOI 10.1109/ICPR.1998.711946