Road sign detection on a smartphone for traffic safety

Abstract

The goal of this work is the development of a low-cost driver assistance system that runs on an ordinary smartphone. It uses computer vision techniques and multiple-resolution template matching to detect speed limit signs and alert the driver if the speed limit is exceeded. It inputs an image of the sign to be detected and creates a set of multiple-resolution templates. It also inputs photographs of the road from the smartphone camera at regular intervals and generates multiple-resolution images from the photographs. In the first step of processing, fast filters restrict the focus of attention to smaller areas of the photographs where signs are likely to be present. In the second step, the system matches the templates against the photographs using fast normalized cross correlation to detect speed limit signs. The multiple resolutions enable this approach to detect signs at different scales. In the third step, the system recognizes the sign by matching a series of annotated speed templates to the image at the position and scale that were determined by the detection step. It compares the speed limit with the actual vehicle speed as computed from the smartphone GPS device and issues warnings to the driver as necessary. The system is implemented as an Android application that runs on an ordinary smartphone as part of a client-server architecture. It processes photos at a rate of 1 Hz with a probability of detection of 0.93 at the 95% confidence level and a false alarm rate of 0.0007, or one false classification every 25 min.