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Detection and classification of traffic signs is one of the most studied Advanced Driver Assistance Systems (ADAS) and some solutions are already installed in vehicles. Nevertheless these systems still have room for improvement in terms of speed and performance. When driving at high speed, warning systems require very fast processing of the video stream in order to lose as few frames as possible and minimize the chance of missing a readable traffic sign. In this paper we show a sign detection system for grayscale images based on a two-stage process: A rapid shape prefiltering, that relies on a new descriptor coined as Local Contour Patterns, rejects most of the image subwindows and preclassifies the rest as one of the three main sign types. Then, a sign-dependent AdaBoost-based cascade detector that makes use of a new set of simpler texture features, coined as Quantum Features, scans the pre-fetched subwindows to fine tune candidate traffic signs. The analysis of this detector over hundreds of video sequences which were captured with a car-mounted 752×480 grayscale camera and provided by the Galician Automotive Technology Center (CTAG) shows a very good behavior for multiclass traffic sign detection running at 14 frames/sec on a 2.8 GHz processor.