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Journal of Electrical and Computer Engineering
Volume 2013, Article ID 391652, 12 pages
Research Article

An Integrative Approach to Accurate Vehicle Logo Detection

1Lucas Varity Langzhong Brake Co., Ltd, Langfang Development Zone, Hebei 065001, China
2Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, SIP, Suzhou 215123, China

Received 9 April 2013; Accepted 4 September 2013

Academic Editor: Mohammad S. Alam

Copyright © 2013 Hao Pan and Bailing Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Vehicle logo detection from images captured by surveillance cameras is an important step towards the vehicle recognition that is required for many applications in intelligent transportation systems and automatic surveillance. The task is challenging considering the small target of logos and the wide range of variability in shape, color, and illumination. A fast and reliable vehicle logo detection approach is proposed following visual attention mechanism from the human vision. Two prelogo detection steps, that is, vehicle region detection and a small RoI segmentation, rapidly focalize a small logo target. An enhanced Adaboost algorithm, together with two types of features of Haar and HOG, is proposed to detect vehicles. An RoI that covers logos is segmented based on our prior knowledge about the logos’ position relative to license plates, which can be accurately localized from frontal vehicle images. A two-stage cascade classier proceeds with the segmented RoI, using a hybrid of Gentle Adaboost and Support Vector Machine (SVM), resulting in precise logo positioning. Extensive experiments were conducted to verify the efficiency of the proposed scheme.