Signboard Recognition by Consistency Checking of Local Features

Abstract

The problem of recognizing signboards in street scenes is defined as matching the input image to pre-stored 2D signboard images. This problem is not as simple as it appears to be due to arbitrary drawings and relative 3D positions. We approached this problem by matching characteristic local features of input image to those of images in the database. Local decisions are verified by the global viewpoint of the homographic consistency and color consistency. The well-known SIFT feature is used as a local feature and the homographic consistency checking is performed using RANSAC, a random sampling method. In order to handle highly perspective-distorted signboards, several perspective-transformed templates are generated offline. In our experiment, with a database of 35 images, our proposed method achieved 95% recognition rate, showing good results despite the highly distorted input images.