Visual Vehicle Identification Using Modern Smart Glasses

Abstract: In recent years wearable devices have been advancing at a rapid pace and one of the largest growing segments is the smart glass segment. In this thesis the feasibility of today’s ARM-based smart glasses are evaluated for automatic license plate recognition (ALPR). The license plate is by far the most prominent visual feature to identify a spe- cific vehicle, and exists on both old and newly produced vehicles. This thesis propose an ALPR system based on a sequence of vertical edge detection, a cascade classifier, verti- cal and horizontal projection as well as a general purpose optical character recognition library. The study further concludes that the optimal input resolution for license plate detection using vertical edges is 640x360 pixels and that the license plate need to be at least 20 pixels high or the characters 15 pixels high in order to successfully segment the plate and recognize each character. The separate stages were successfully implemented into a complete ALPR system that achieved 79.5% success rate while processing roughly 3 frames per second when running on a pair of Google Glass.