Real-time Speed Limit Sign Recognition Based on Locally Adaptive Thresholding and Depth-First-Search

Abstract

Road inventory for a highway includes a variety of traffic signs, such as stop signs and speed limit signs, pavement width, lane number, and others. This article describes a current research project being undertaken by the Georgia Department of Transportation to re-engineer and streamline the existing road inventory data collection process, in order to develop a real-time data collection system that is accurate, efficient, and safe. The authors present an algorithm for recognizing speed limit signs (SLS) from video imaging and extracting the numerical numbers of SLS to support real-time road inventory data collection operations. The algorithm consists of color segmentation based on locally adaptive thresholding extraction of regions of interest (ROI) using a depth-first-search algorithm, followed by speed limit sign detection and speed limit number extraction by means of optical character recognition and 2D correlation. The average processing time for an image of 1200 x 800 pixels is about 125 ms. Experimental results from 1,401 video images show 0 percent false positives out of 1,278 images containing no SLS, and 3 percent false negatives out of 123 images containing SLS. Signs that are tilted up to 15 degrees and surrounded by white and non-white backgrounds can be correctly detected independent of lighting conditions. Partly shadowed, partly blocked, or severely blurred SLSs (due to a fast-moving camera) would not be correctly detected.