Share Email Print
cover

Proceedings Paper • new

Vehicle color recognition based on superpixel features
Author(s): Qiuli Lin; Feng Liu; Qiang Zhao; Ran Xu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this paper, a novel methodology is presented to settle the region of interest (ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color recognition. In order to make full use of the local color and spatial information, vehicle images are divided into different superpixels at first. The spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle images. By comparing with the vehicle window clustering centroids obtained by k-means, the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are determined. Finally, a linear Support Vector Machine classifier is trained for color recognition. The experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791G (14 August 2019); doi: 10.1117/12.2539809
Show Author Affiliations
Qiuli Lin, Nanjing Univ. of Posts and Telecommunications (China)
Feng Liu, Nanjing Univ. of Posts and Telecommunications (China)
Qiang Zhao, Nanjing Univ. of Posts and Telecommunications (China)
Ran Xu, Nanjing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top