Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model-Reference-Cited by-同舟云学术

Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model

Published:2018-10-02 Issue:10 Volume:12 Page:1283-1290
ISSN:1751-9578
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Transport Systems

Author:

Zhang Yunsheng¹²³,Zhao Chihang⁴,Shi Wen²,Leng Kaijun²

Affiliation:

1. The National Key Lab of Big Data Exchange Tech. and Analysis of NDRC (Development and Reform Commission), School of Management, Xi'an Jiaotong UniversityXi'an710049ShaanxiPeople's Republic of China

2. Ressearch Center of Hubei Logistics Development, Hubei University of EconomicsWuhan430205People's Republic of China

3. Collaborative Innovation Center of China Pilot Reform Exploration and Assessment – Hubei Sub‐Center, Hubei University of EconomicsWuhan430205HubeiPeople's Republic of China

4. College of Transportation, Southeast UniversityNanjing210096People's Republic of China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2018.5298

Reference34 articles.

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3. Illumination‐invariant background subtraction: comparative review, models, and prospects;Kim W.;IEEE. Access,2017

4. A Review of Computer Vision Techniques for the Analysis of Urban Traffic

5. StaufferC.GrimsonW. E. L.: ‘Adaptive background mixture models for real‐time tracking’.IEEE Computer Society Conf. on Computer Vision and Pattern Recognition 1999 vol. 2 pp.246–252

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