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Erschienen in:
2010 | OriginalPaper | Buchkapitel
A Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System Using GPU Computing
verfasst von : Pınar Muyan-Özçelik, Vladimir Glavtchev, Jeffrey M. Ota, John D. Owens
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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We present a template-based pipeline that performs realtime speed-limit-sign recognition using an embedded system with a lowend GPU as the main processing element. Our pipeline operates in the frequency domain, and uses nonlinear composite filters and a contrastenhancing preprocessing step to improve its accuracy. Running at interactive rates, our system achieves 90% accuracy over 120 EU speed-limit signs on 45 minutes of video footage, superior to the 75% accuracy of a non-real-time GPU-based SIFT pipeline.