Review on Vehicle Detection Based on Video Processing
International Journal of Science, Technology and Society
Volume 5, Issue 4, July 2017, Pages: 126-130
Received: May 19, 2017;
Accepted: Jun. 2, 2017;
Published: Jul. 18, 2017
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Jiao Zhiyuan, Automobile Engineering College, Shanghai University Engineering Science, Shanghai, China
Xing Yanfeng, Automobile Engineering College, Shanghai University Engineering Science, Shanghai, China
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Compared with traditional vehicle detectors, video sensor has lots of advantages, i.e., easy installation and maintenance, wide monitoring areas, obtaining more kinds of traffic parameters and etc, so it has been widely used in Intelligent Traffic Systems. On this basis, discuss about the vehicle detection methods based on feature, model checking, frame difference, optical flow field. At the same time, the verification method is introduced, and the advantages and disadvantages of various algorithms are analyzed and compared. Finally, some suggestions for future research and application are presented, for example, vehicle detection is carried out by using a variety of detection methods and multi detector information fusion.
Intelligent Transportation System, Vehicle Detection, Monocular Vision
To cite this article
Review on Vehicle Detection Based on Video Processing, International Journal of Science, Technology and Society.
Vol. 5, No. 4,
2017, pp. 126-130.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
M Tomizuka. Automated Highway Systems: An Intelligent Transportation System for the Next Century [J]. IEEE International Symposium on Industrial Electronics, 1997, (1): 1-4.
LIU T, ZHENG N, ZHAO L. Learning based Symmetric Features Selection for Vehicle Detection [C]. IEEE Proceedings of Intelligent Vehicles Symposium. IEEE, 2005: 124-129.
CHARKARI N M, MORI H. A New Approach for Real Time Moving Vehicle Detection [C] Proceedings of the IEEE RSJ International Conference on Intelligent Robots and Systems. 1993, 1: 273- 278.
KUT SUMA Y, YA GUCHI H, HAMA MOTO T. Real time Lane Line and Forward Vehicle Detection by Smart Image Sensor [C]. IEEE International Symposium on Communications and Information Technology. IEEE, 2004, 2: 957- 962.
HOFFMAN C, DANG T, STILLER C. Vehicle detection fusing 2D visual features [C]. IEEE Proceedings of Intelligent Vehicles Symposium. IEEE, 004: 280-285.
CLADY X, COLLANGE F, JURIE F. Cars detection and tracking with a vision sensor [C]. IEEE Proceedings of Intelligent Vehicles Symposium. IEEE, 2003: 593- 598.
M P Dubuisson, S Lakshmanan, A K Jain. Vehicle Segmentation and Classification Using Deformable Templates [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18 (3): 293-308.
J Ferryman, A Worrall. A Generic Deformable Model for Vehicle Recognition [C]. Proceedings of British Machine Vision Conference, 1995. 127-136.
ABUTALEB A S. Automatic Thresh holding of Gray level Pictures Using Two-dimensional Entropy [J]. Computer Vision Graphics and Image Processing, 1989, 47 (2): 22- 32.
PARKY. Shapere solving Local Thresh holding for Object Detection [J]. Pattern Recognition Letters, 2001, 22 (5): 883- 890.
HORN BK P, SCHUNCK BG. Determining optical flow [J]. Artificial Intelligence, 1981, 17 (1): 185- 203.
SINGH A, ALLEN P. Image flow computation: an estimation theoretic framework and a unified perspective [C]. Proc of CVG IP: Image Understanding. 1992, 56: 152- 177.
HEEGER D J. Model for the extraction of image flow [J]. J Opt Soc Am, 1987 (4): 1455- 1471.
FLEETDJ, JESPONAD. Computation of component image velocity from local phase information [J]. International Journal of Computer Vision, 1990, 5 (1): 77- 104.