[[abstract]]This paper describes an approach to detecting road signs from complex backgrounds. The input data are color image sequences acquired using a single camcorder mounted on a moving vehicle. Two neural networks are developed to extract color and shape features, respectively, from image sequences. A process characterized by fuzzy discipline is then introduced to determine road sign candidates based on the extracted color and shape features. Experimental results have manifested the applicability of the proposed method. Incremental strategies applied to constantly incoming video data may improve the performance of the method.
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