In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions extraction rather than a sliding window detection scheme. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of Mandatory, Prohibitory and Danger traffic signs. Our proposal has been evaluated experimentally within the German Traffic Sign Detection Benchmark competition.

Samuele Salti, Alioscia Petrelli, Federico Tombari, Nicola Fioraio, Luigi Di Stefano (2013). A traffic sign detection pipeline based on interest region extraction. IEEE [10.1109/IJCNN.2013.6706808].

A traffic sign detection pipeline based on interest region extraction

SALTI, SAMUELE;PETRELLI, ALIOSCIA;TOMBARI, FEDERICO;FIORAIO, NICOLA;DI STEFANO, LUIGI
2013

Abstract

In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions extraction rather than a sliding window detection scheme. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of Mandatory, Prohibitory and Danger traffic signs. Our proposal has been evaluated experimentally within the German Traffic Sign Detection Benchmark competition.
2013
Proc. of the 2013 International Joint Conference on Neural Networks (IJCNN)
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Samuele Salti, Alioscia Petrelli, Federico Tombari, Nicola Fioraio, Luigi Di Stefano (2013). A traffic sign detection pipeline based on interest region extraction. IEEE [10.1109/IJCNN.2013.6706808].
Samuele Salti; Alioscia Petrelli; Federico Tombari; Nicola Fioraio; Luigi Di Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/315321
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