Volume 8 Number 8 (Aug. 2013)
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JSW 2013 Vol.8(8): 1952-1959 ISSN: 1796-217X
doi: 10.4304/jsw.8.8.1952-1959

An Efficient Vehicle Model Recognition Method

Huihua Yang1, Lei Zhai2, Lingqiao Li2, Zhenbing Liu2, Yichen Luo2, Yong Wang2, Haiyan Lai3, Ming Guan3
1Guangxi Experiment Center of Information Science, Guilin, China
2School of Electronic Engineering and Automation/Guilin University of Electronic Technology, Guilin, China
3Guangxi Communications Investment Group CO., LTD, Guilin, China

Abstract—An efficient vehicle model recognition method based on Adaptive Harris corner detector is presented in this paper. First, the vehicle radiator grid is selected as ROI and Harris corner detection is used to detect corner as vehicle model features, to solve a problem of inconsistencies in the number of corner between different models or the same model in different environment. Second, an adaptive threshold function is constructed to control the number of corner replacing a fixed threshold, ensuring that the image is always able to produce a certain number of strong corners. Third, a parallel scheme is designed to accelerate the vehicle recognition algorithm via GPU/CPU heterogeneous computing model to meet real-time requirement, which includes parallelization of algorithm and parallelization of process. The experiments on 1096 big truck images of 12 vehicle models obtain the recognition accuracy rate of 99.5%, and achieve 58x speedup on average by a platform with Intel Core i5 2400 and NVIDIA C2075. The results show that our proposed method can meet the requirements of practical application.

Index Terms—Vehicle model recognition; ROI positioning; adaptive Harris Algorithm; GPU/CPU collaborative computing.

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Cite: Huihua Yang, Lei Zhai, Lingqiao Li, Zhenbing Liu, Yichen Luo, Yong Wang, Haiyan Lai, Ming Guan, "An Efficient Vehicle Model Recognition Method," Journal of Software vol. 8, no. 8, pp. 1952-1959, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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