Traffic Sign Recognition based on Synthesised Training Data
Stergiou, A, Kalliatakis, G and Chrysoulas, C (2018). Traffic Sign Recognition based on Synthesised Training Data. Big Data and Cognitive Computing. 2 (3), p. 19.
|Authors||Stergiou, A, Kalliatakis, G and Chrysoulas, C|
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.
|Journal||Big Data and Cognitive Computing|
|Journal citation||2 (3), p. 19|
|Publisher||London South Bank University|
|Digital Object Identifier (DOI)||doi:10.3390/bdcc2030019|
|27 Jul 2018|
|Publication process dates|
|Deposited||06 Aug 2018|
|Accepted||24 Jul 2018|
|Accepted author manuscript|
CC BY 4.0
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