Road Sign Detection is a major goal of the Advanced Driving Assistance Systems. Most published work on this problem share the same approach by which signs are first detected and then classified in video sequences, even if different techniques are used. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel modular and scalable approach to road sign detection based on Particle Swarm Optimization, which takes into account both shape and color to detect signs. In our approach, in particular, the optimization of a single fitness function allows both to detect a sign belonging to a certain category and, at the same time, to estimate its position with respect to the camera reference frame. To speed up processing, the algorithm implementation exploits the parallel computing capabilities offered by modern graphics cards and, in particular, by the Compute Unified Device Architecture by nVIDIA. The effectiveness of the approach has been assessed on both synthetic and real video sequences, which have been successfully processed at, or close to, full frame rate.
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