@inproceedings{Muyan-Ozcelik:2010:ATA,
| title | = | "A Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing", |
| booktitle | = | "DAGM (The German Association for Pattern Recognition) Symposium", |
| author | = | "Pinar
Muyan-Ozcelik AND Vladimir
Glavtchev AND Jeffrey
M. Ota AND John
D. Owens ", |
| year | = | "2010", |
| keywords | = | "GPU computing, computer vision, embedded systems, real-time automotive computing applications", |
| pages | = | "162–171", |
| organization | = | "DAGM (The German Association for Pattern Recognition) Symposium", |
| publisher | = | "Springer Series on Lecture Notes in Computer Science (LNCS)", |
| location | = | "Darmstadt, Germany", |
| eventtime | = | "September 22-24, 2010", |
| 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.", |
