An image analysis technique for automatic traffic sign detection and classification is proposed. It makes it possible, after proper training, to detect, recognize and classify vertical road signs from video frames acquired on a moving vehicle equipped with cameras, as well as to identify anomalies with respect to road sign regulations (positioning and visibility). The experimental results show that this technique allows one to correctly detect and classify almost all vertical signs and, mainly in extra-urban environment, it can be considered as highly reliable, apart from being really versatile and user-friendly for road inventory and road maintenance purposes.

Bruno, L., Parla, G., Celauro C. (2012). Improved Traffic Signal Detection and Classification via Image Processing Algorithms. PROCEDIA: SOCIAL & BEHAVIORAL SCIENCES, 53, 811-821 [10.1016/j.sbspro.2012.09.930].

Improved Traffic Signal Detection and Classification via Image Processing Algorithms

BRUNO, Leonardo;PARLA, Giuseppe;CELAURO, Clara
2012-01-01

Abstract

An image analysis technique for automatic traffic sign detection and classification is proposed. It makes it possible, after proper training, to detect, recognize and classify vertical road signs from video frames acquired on a moving vehicle equipped with cameras, as well as to identify anomalies with respect to road sign regulations (positioning and visibility). The experimental results show that this technique allows one to correctly detect and classify almost all vertical signs and, mainly in extra-urban environment, it can be considered as highly reliable, apart from being really versatile and user-friendly for road inventory and road maintenance purposes.
2012
Settore ICAR/04 - Strade, Ferrovie Ed Aeroporti
Bruno, L., Parla, G., Celauro C. (2012). Improved Traffic Signal Detection and Classification via Image Processing Algorithms. PROCEDIA: SOCIAL & BEHAVIORAL SCIENCES, 53, 811-821 [10.1016/j.sbspro.2012.09.930].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/69626
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