SVM-based Obstacles Recognition for Road Vehicle Applications

This paper describes an obstacle Recognition System based on SVM and vision. The basic components of the detected objects are first located in the image and then combined with a SVM-based classifier. A distributed learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road images has been created for learning purposes. We present and discuss the results achieved up to date.

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