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Big Data Cogn. Comput. 2018, 2(3), 19; https://doi.org/10.3390/bdcc2030019

Traffic Sign Recognition based on Synthesised Training Data

1
Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
2
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
3
School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 18 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Abstract

To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of existing traffic sing recognition databases, which are only subject to specific sets of road signs found explicitly in countries or regions. This approach is used for generating a database of synthesised images depicting traffic signs under different view-light conditions and rotations, in order to simulate the complexity of real-world scenarios. With our synthesised data and a robust end-to-end Convolutional Neural Network (CNN), we propose a data-driven, traffic sign recognition system that can achieve not only high recognition accuracy, but also high computational efficiency in both training and recognition processes. View Full-Text
Keywords: traffic sign recognition; synthetic data; dataset generator; CNNs traffic sign recognition; synthetic data; dataset generator; CNNs
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Stergiou, A.; Kalliatakis, G.; Chrysoulas, C. Traffic Sign Recognition based on Synthesised Training Data. Big Data Cogn. Comput. 2018, 2, 19.

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