NDLI: GPU implementation of a road sign detector based on particle swarm optimization

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GPU implementation of a road sign detector based on particle swarm optimization

Content Provider	Springer Nature Link
Author	Mussi, Luca Cagni, Stefa Cardarelli, Elena Daolio, Fabio Medici, Paolo Porta, Pier Paolo
Copyright Year	2010
Abstract	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.
Starting Page	155
Ending Page	169
Page Count	15
File Format	PDF
ISSN	18645909
Journal	Evolutionary Intelligence
Volume Number	3
Issue Number	3-4
e-ISSN	18645917
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-10-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Particle swarm optimization Road sign detection GPU computing Parallel computing Applications of Mathematics Bioinformatics Control Robotics Mechatronics Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) ApplicationMathematics/Computational Methods of Engineering
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Cognitive Neuroscience Mathematics Computer Vision and Pattern Recognition

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