Special Section on Video Surveillance and Transportation Imaging Applications

Traffic sign recognition based on a context-aware scale-invariant feature transform approach

[+] Author Affiliations
Xue Yuan

Beijing Jiaotong University, School of Electronic and Information Engineering, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China

Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Beijing 100830, China

Xiaoli Hao

Beijing Jiaotong University, School of Electronic and Information Engineering, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China

Houjin Chen

Beijing Jiaotong University, School of Electronic and Information Engineering, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China

Xueye Wei

Beijing Jiaotong University, School of Electronic and Information Engineering, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China

J. Electron. Imaging. 22(4), 041105 (Jul 15, 2013). doi:10.1117/1.JEI.22.4.041105
History: Received October 30, 2012; Revised June 3, 2013; Accepted June 5, 2013
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Abstract.  A new context-aware scale-invariant feature transform (CASIFT) approach is proposed, which is designed for the use in traffic sign recognition (TSR) systems. The following issues remain in previous works in which SIFT is used for matching or recognition: (1) SIFT is unable to provide color information; (2) SIFT only focuses on local features while ignoring the distribution of global shapes; (3) the template with the maximum number of matching points selected as the final result is instable, especially for images with simple patterns; and (4) SIFT is liable to result in errors when different images share the same local features. In order to resolve these problems, a new CASIFT approach is proposed. The contributions of the work are as follows: (1) color angular patterns are used to provide the color distinguishing information; (2) a CASIFT which effectively combines local and global information is proposed; and (3) a method for computing the similarity between two images is proposed, which focuses on the distribution of the matching points, rather than using the traditional SIFT approach of selecting the template with maximum number of matching points as the final result. The proposed approach is particularly effective in dealing with traffic signs which have rich colors and varied global shape distribution. Experiments are performed to validate the effectiveness of the proposed approach in TSR systems, and the experimental results are satisfying even for images containing traffic signs that have been rotated, damaged, altered in color, have undergone affine transformations, or images which were photographed under different weather or illumination conditions.

© 2013 SPIE and IS&T

Citation

Xue Yuan ; Xiaoli Hao ; Houjin Chen and Xueye Wei
"Traffic sign recognition based on a context-aware scale-invariant feature transform approach", J. Electron. Imaging. 22(4), 041105 (Jul 15, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041105


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