Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 15, Issue 6, Pages 2394-2403Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2014.2314711
Keywords
Common-Finder AdaBoost (CF.AdaBoost); multiblock normalization local binary pattern (MN-LBP); multiclass object detection; split-flow cascade; traffic sign detection (TSD)
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Funding
- National Natural Science Foundation of China [61273277, 61203261]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130131110038]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [20101174]
- Shandong Province Natural Science Foundation Committee [ZR2011FM032]
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This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.
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