|PROJECTS AT VISL FINISHED IN 2006|
The ProblemThe streets are a jungle. Different Road signs may appear from anywhere in front of the driver. An automatic system that can overcome all these problems and alerts the driver to the presence of these signs can prevent accidents from happening. It might save money and even lives.
The algorithm follows these classification steps:
The steps of the algorithm are:
The final statistics of the algorithm, without considering video advantage:
Classification error - cases in which the algorithm confuses two different signs.
Correlation error - cases in which the correlation of the sign was too low for the algorithm to determine the correct sign.
When considering the fact that a sign can be classified correctly in at least one frame, the final statistics are:
Considering the bad video conditions we faced during the entire project, these results are satisfying.
The algorithm was created in Matlab-7.0 enviorment. In addition, the OSU-SVM toolbox was used.
A video camera was used to capture the movies and create the data base.
An algorithm that detects road signs and alerts to their presence was created, in Matlab environment. If connected to a suitable camera and implemented in fast environmentthis algorithm can work in Real-Time to detect road signs.
The algorithm can overcome problems such as: shadow, different shades of the colors in the sign, partly concealed signs, some disfigure of the sign and appearance from different angles.
In addition, a special effort was made in order to improve the Run-Time of the algorithm. This effort includes some compromises in regard to the system performance
An example of a graphical interface that the algorithm produces:
See demo movie ! (80Mb)
We would like to thank our supervisor Dori Peleg for his support and guidance throughout this project.
Also we would like to thank Johanan Erez and Ina Krinski that helped us in every technical aspect and hardware issues.
FULL DOCUMENTATION Finally we would like to thank the Ollendorff
Minerva Center for supporting the project.
Finally we would like to thank the Ollendorff Minerva Center for supporting the project.