Knowledge Template Based Multi-perspective Car Recognition Algorithm

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Author(s)

Bo Cai 1,* Feng Tan 1 Yi Lu 1 Dengyi Zhang 1

1. School of Computer, Wuhan University, Wuhan 430072, Hubei, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2010.02.06

Received: 15 Sep. 2010 / Revised: 16 Oct. 2010 / Accepted: 2 Nov. 2010 / Published: 8 Dec. 2010

Index Terms

Template matching, line extraction, vehicle detection, Fourier descriptors, Chain code, Round rate, Circumference ratio

Abstract

In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS) is raised and become scientist’s research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it’s the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.

Cite This Paper

Bo Cai, Feng Tan, Yi Lu, Dengyi Zhang, "Knowledge Template Based Multi-perspective Car Recognition Algorithm", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.2, no.2, pp.38-45, 2010. DOI:10.5815/ijieeb.2010.02.06

Reference

[1]Z.Kim. Realtime Obstacle Detection and Tracking Based on Constrained Delaunay Triangulation, IEEE Intelligent Transportation System Conference, pp.548-553,2006
[2]Zehang Sun, George Bebis and Ronald Miller. On-road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE transactions on ITS. 2005
[3]Luigi Di Stefano, Enrico Viarani. Vehicle Detection and Tracking Using the Block Matching Algorithm. Proc. of 3rd IMACS/IEEE. 1999
[4]J.M.Collado, C.Hilario. Model Based Vehicle Detection for Intelligent Vehicles. IEEE Intelligent Vehicles Symposium. 2004
[5]M.Bertozzi , A.Broggi, A.Fascioli. Stereo Vision-based Vehicle Detection.IEEE INTELLIGENT VEHICLES SYMPOSIUM. 2000
[6]Yi Lu, Bo Cai, Dengyi Zhang. Contour based car recognition algorithm. CNMT2009
[7]Zehang Sun, George Bebis and Ronald Miller. On-road vehicle detection: a review. IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, no.5.2006
[8]Ronan O’Malley, Martin Glavin. Vehicle Detection at Night Based on Tail-Light Detection. National University of Ireland,Galway.
[9]Marie-Pierre Dubuisson Jolly. Vehicle Segmentation and Classification Using Deformable Templates. IEEE transactions on pattern analysis and machine intelligence, Vol 18, No.3 1996
[10]Bo Cai, Dongru Zhou. Content based video classification and retrieval [D].Wuhan University 2003.10(in Chinese)