High-definition and highly accurate road maps are necessary for the realization of automated driving, and roadsigns are among the most important element in the road map. Therefore, a technique is necessary which can ...
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High-definition and highly accurate road maps are necessary for the realization of automated driving, and roadsigns are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of roadsigns automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of roadsigns from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of roadsigns, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.
An automatic roadsignsrecognition system can be integrated in the vehicles of the future to improve drivers' safety and driver-road interactions supplying warning mechanisms for drivers' assistance. We prese...
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ISBN:
(纸本)0819428132
An automatic roadsignsrecognition system can be integrated in the vehicles of the future to improve drivers' safety and driver-road interactions supplying warning mechanisms for drivers' assistance. We present an efficient expert system for the road sign recognition task: roadsigns are detected taking into account their color and shape, whereas roadsignsrecognition is performed by MLP neural network classifiers. In the paper are also reported the experimental results obtained by the proposed system on real outdoor road images.
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