Adaptive Difference of Gaussian for Speed Sign Detection in Night-time Conditions
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 . Many techniques have been explored, including artificial neural network (ANN) [2, 3], Support Vector Machine (SVM) , gradient-based detection [5–7], geometric labeling  and signature (FFT) matching . 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.
KeywordsVideo Sequence Radial Symmetry Motion Blur Speed Sign Pedestrian Detection
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