Abstract
The recognition of traffic signs in natural environment is a challenging task in computer vision because of the influence of weather conditions, illuminations, locations, vandalism, and other factors. In this paper, we propose a vision-based traffic sign recognition system for the real utilization of intelligent vehicles. The proposed system consists of two phases: detection and recognition. In detection phase, we employ simple vector filter for chromatic/achromatic discrimination and color segmentation followed by shape analysis to roughly divide traffic signs into seven categories according to the color and shape properties. The Pseudo-Zernike moments features of the extracted candidate traffic sign regions are selected for recognition by random forests which combines bootstrap aggregating (bagging) algorithm and random feature selection to construct collections of decision trees and possesses excellent classification ability. The rationality and effectiveness of the proposed system is validated on our intelligent vehicle—Intelligent Pioneer from a great number of experiments.
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This work was supported by National Natural Science Foundation of China under grant No. 91120307 and 60875076.
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Yang, J., Kong, B., Wang, B. (2014). Vision-Based Traffic Sign Recognition System for Intelligent Vehicles. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_31
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DOI: https://doi.org/10.1007/978-3-642-37835-5_31
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