JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE)

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A comprehensive method for traffic lights detection in complex background

SI Junshu, ZHU Wenxing*,  SHA Yonghe   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, China
  • Received:2013-11-05 Online:2014-04-20 Published:2013-11-05

Abstract: To solve the auto-recognition problem of multi-type traffic lights in complex background, a novel method based on brightness value division,K-means clustering and histogram analysis of foreground objects was proposed. Firstly, self-localization was realized by brightness value division, geometric features analysis and classification statistics. Along with signal color judgment by K-means clustering algorithm, the type and direction information of traffic lights would be acquired by analyzing the histograms of foreground objects; accordingly auto-detection was realized. Experimental results showed that the proposed method had high detection accuracy for multi-type traffic lights in different scenarios. It was proved that this method was highly reliable and widely applicable.

Key words: self-localization, traffic light recognition, brightness value division, K-means clustering, foreground object histogram

CLC Number: 

  • TP391
[1] DING Na-na, TIAN Guo-hui*, LI Guo-dong, ZHANG Qing-bin. Visual self-localization of biped robot based on artificial landmark [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(4): 51-56.
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