Rapid vehicle logo region detection based on information theory

Mao, Songan, Ye, Mao, Li, Xue, Pang, Feng and Zhou, Jinglei (2013) Rapid vehicle logo region detection based on information theory. Computers and Electrical Engineering, 39 3: 863-872. doi:10.1016/j.compeleceng.2013.03.004

Author Mao, Songan
Ye, Mao
Li, Xue
Pang, Feng
Zhou, Jinglei
Title Rapid vehicle logo region detection based on information theory
Journal name Computers and Electrical Engineering   Check publisher's open access policy
ISSN 0045-7906
Publication date 2013-04
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.compeleceng.2013.03.004
Open Access Status
Volume 39
Issue 3
Start page 863
End page 872
Total pages 10
Place of publication Oxford, United Kingdom
Publisher Pergamon
Collection year 2014
Language eng
Abstract Vehicle logo detection is an important task in intelligent transportation systems. In this paper, a novel method is proposed for detecting the vehicle logo in an image. Our method consists of three main steps. First, horizontal and vertical direction filters are applied to the original image to produce two new images. Then, a saliency map is generated from each image. Second, two clusters in the corresponding saliency map are formed to create a binary image. Finally, the vehicle logo is localized by searching the regions with the maximum useful information. Our method has two main contributions. One is that the vehicle logo can be detected rapidly without learning. The other is that our method is adaptable to different situations without adjusting the parameters. A series of experiments are performed on 970 images, which are captured from different real-time situations. Experimental results show that our method is also very fast and can achieve a high detection rate, which is suitable for real-time applications.
Keyword Vehicle logo detection
Rapid detection
Real-time situations
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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