Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. 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, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data.

Traffic sign detection via interest region extraction / Samuele Salti;Alioscia Petrelli;Federico Tombari;Nicola Fioraio;Luigi Di Stefano. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - STAMPA. - 48:4(2015), pp. 1039-1049. [10.1016/j.patcog.2014.05.017]

Traffic sign detection via interest region extraction

SALTI, SAMUELE;PETRELLI, ALIOSCIA;TOMBARI, FEDERICO;FIORAIO, NICOLA;DI STEFANO, LUIGI
2015

Abstract

Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. 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, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data.
2015
Traffic sign detection via interest region extraction / Samuele Salti;Alioscia Petrelli;Federico Tombari;Nicola Fioraio;Luigi Di Stefano. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - STAMPA. - 48:4(2015), pp. 1039-1049. [10.1016/j.patcog.2014.05.017]
Samuele Salti;Alioscia Petrelli;Federico Tombari;Nicola Fioraio;Luigi Di Stefano
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/387072
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 107
  • ???jsp.display-item.citation.isi??? 81
social impact