Traffic sign shape classification based on Support Vector Machines and the FFT of the signature of blobs

In many traffic sign recognition systems, one of the main tasks is the classification of the shape of the blob, which is intended to simplify the recognition process. In this paper, we have developed a new shape classification algorithm based on Support Vector Machines classifiers and the FFT of the signature of the blob. The FFT of the signature yields invariance to object scalings and rotations. Furthermore, the FFT is the vector input to the classifier. This classifier is trained to cope with projection deformations and occlusions. The algorithm has been tested under adverse conditions, such as geometric distortions, i.e. scaling, rotations and projection deformations, and occlusions. The experimental results show good robustness when the system is working with real, outdoor road images.