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Adaptive Difference of Gaussian for Speed Sign Detection in Night-time Conditions

  • Tam Phuong Cao
  • Darrell M. Elton
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

Road safety is gaining more public attention nowadays. Safety technologies, such as air bags or antilock-braking systems (ABS), have been providing a much higher level of safety to road traffic participants. Intelligent vehicles that can actively help to prevent accidents are also being investigated and developed. One area of research is in speed sign detection systems that alert the driver to the current speed limit.

Vision-based systems that are able to detect and classify speed signs have been under investigation since the late 1980s [1]. Many techniques have been explored, including artificial neural network (ANN) [2, 3], Support Vector Machine (SVM) [4], gradient-based detection [5–7], geometric labeling [8] and signature (FFT) matching [9]. Most of those algorithms perform well in a limited range of lightning conditions, which are normally restricted to daytime lighting conditions. In order to be reliably integrated into an automobile, any vision system needs to be able to run accurately and in real-time regardless of weather and lighting conditions.

Keywords

Video Sequence Radial Symmetry Motion Blur Speed Sign Pedestrian Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Tam Phuong Cao
    • 1
  • Darrell M. Elton
    • 1
  1. 1.Department of Electronic EngineeringLatrobe UniversityAustralia

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