Rapid Multiclass Traffic Sign Detection in High-Resolution Images.

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 <bold>multiblock normalization local binary pattern</bold> (MN-LBP) and <bold>tilted MN-LBP</bold> (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called <bold>split-flow cascade</bold>, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the <bold>Common-Finder AdaBoost</bold> (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient <bold>Split-Flow Cascade tree</bold> (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. [ABSTRACT FROM PUBLISHER]