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| Title | A Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
(In Proceedings) |
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| in | DAGM (The German Association for Pattern Recognition) Symposium |
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| Author(s) |
Pinar Muyan-Ozcelik, Vladimir Glavtchev, Jeffrey M. Ota, John D. Owens |
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| Keyword(s) | GPU computing, computer vision, embedded systems, real-time automotive computing applications |
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| Year |
2010
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| Location | Darmstadt, Germany |
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| Date | September 22-24, 2010 |
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| Publisher | Springer Series on Lecture Notes in Computer Science (LNCS) |
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| Organization | DAGM (The German Association for Pattern Recognition) Symposium |
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| Pages | 162–171 |
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| Abstract |
We present a template-based pipeline that performs real-time speed-limit-sign recognition using an embedded system with a low-end GPU as the main processing element. Our pipeline operates in the frequency domain, and uses nonlinear composite filters and a contrast-enhancing 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.
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