Generalized Sign Recognition Based on the Gaussian Statistical Color Model for Intelligent Road Sign Inventory

Abstract

The rapid progress in sensor and information technologies makes it possible to develop intelligent road sign inventory (IRSI) by automatically collecting and processing vehicle-borne multisensor data. This study developed a framework for IRSI with a vehicle-borne multisensor system. The IRSI framework consists of four modules: (a) sensor vehicle design for multisensor data collection, (b) sign recognition, (c) sign data integration and attribute computation, and (d) IRSI-based applications. Within the framework, sign recognition is the key to IRSI. The study proposed a novel Gaussian statistical color model (G-SCM) for image segmentation and color feature extraction. The G-SCM has good capability to model actual sign color distributions and has fast implementation for G-SCM-based color segmentation. With color features integrated into sign texture, shape, location, and other features, a novel generalized recognition algorithm is proposed for sign inventory. The proposed G-SCM model and the sign recognition method were tested with actual video log images. The experimental results show that the G-SCM can model actual sign colors and segment sign images accurately. The fast implementation can greatly enhance computation speed compared with existing color analysis methods. The developed sign recognition algorithm was tested to recognize all speed limit signs from video log images collected along a 140-km highway segment. The algorithm recognized 30 of 31 signs and achieved a 96.8% site recognition rate. Of the 136 images containing speed limit signs, the algorithm had only eight false positives. The results demonstrate that the proposed sign recognition method is promising for the development of IRSI.

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