Special Section on Intelligent Surveillance for Transport Systems

Multiview road sign detection via self-adaptive color model and shape context matching

[+] Author Affiliations
Chunsheng Liu, Faliang Chang, Chengyun Liu

Shandong University, School of Control Science and Engineering, 17923 Jingshi Road, Jinan 250061, China

J. Electron. Imaging. 25(5), 051202 (Mar 23, 2016). doi:10.1117/1.JEI.25.5.051202
History: Received July 1, 2015; Accepted November 25, 2015
Text Size: A A A

Abstract.  The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions.

Figures in this Article
© 2016 SPIE and IS&T

Topics

Roads ; Sensors

Citation

Chunsheng Liu ; Faliang Chang and Chengyun Liu
"Multiview road sign detection via self-adaptive color model and shape context matching", J. Electron. Imaging. 25(5), 051202 (Mar 23, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.051202


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.