[[abstract]]In a visual driver-assistance system, road sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detecting phase, two neural networks are developed to extract color and shape features of traffic signs, respectively, from the input scene images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, the traffic signs located in the previous phase are tracked through image sequences by using the Kalman filter. The experimental results demonstrate that the proposed method performs well in detecting and tracking road signs in complex scenes and in various weather and illumination conditions.
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