TitleA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing (In Proceedings)
inDAGM (The German Association for Pattern Recognition) Symposium
Author(s) Pinar Muyan-Ozcelik, Vladimir Glavtchev, Jeffrey M. Ota, John D. Owens
Keyword(s)GPU computing, computer vision, embedded systems, real-time automotive computing applications
Year 2010
LocationDarmstadt, Germany
DateSeptember 22-24, 2010
PublisherSpringer Series on Lecture Notes in Computer Science (LNCS)
OrganizationDAGM (The German Association for Pattern Recognition) Symposium
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.