Road Sign Detection is a major goal of Advanced Driving Assistance Systems (ADAS). Since the dawn of this discipline, much work based on different techniques has been published which shows that traffic signs can be first detected and then classified in video sequences in real time. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel approach based on both sign shape and color which uses Particle Swarm Optimization (PSO) for detection. Remarkably, a single fitness function can be used both to detect a sign belonging to a certain category and, at the same time, to estimate its actual position with respect to the camera reference frame. To speed up execution times, the algorithm exploits the parallelism offered by modern graphics cards and, in particular, the CUDA™ architecture by nVIDIA. The effectiveness of the approach has been assessed on a synthetic video sequence, which has been successfully processed in real time at full frame rate.

GPU-Based Road Sign Detection Using Particle Swarm Optimization / Mussi, Luca; Cagnoni, Stefano; Daolio, Fabio. - (2009), pp. 152-157. (Intervento presentato al convegno Ninth International Conference on Intelligent Systems Design and Applications (ISDA) tenutosi a Pisa nel 30/11-2/12/2009) [10.1109/ISDA.2009.88].

GPU-Based Road Sign Detection Using Particle Swarm Optimization

MUSSI, LUCA;CAGNONI, Stefano;DAOLIO, FABIO
2009-01-01

Abstract

Road Sign Detection is a major goal of Advanced Driving Assistance Systems (ADAS). Since the dawn of this discipline, much work based on different techniques has been published which shows that traffic signs can be first detected and then classified in video sequences in real time. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel approach based on both sign shape and color which uses Particle Swarm Optimization (PSO) for detection. Remarkably, a single fitness function can be used both to detect a sign belonging to a certain category and, at the same time, to estimate its actual position with respect to the camera reference frame. To speed up execution times, the algorithm exploits the parallelism offered by modern graphics cards and, in particular, the CUDA™ architecture by nVIDIA. The effectiveness of the approach has been assessed on a synthetic video sequence, which has been successfully processed in real time at full frame rate.
2009
9780769538723
GPU-Based Road Sign Detection Using Particle Swarm Optimization / Mussi, Luca; Cagnoni, Stefano; Daolio, Fabio. - (2009), pp. 152-157. (Intervento presentato al convegno Ninth International Conference on Intelligent Systems Design and Applications (ISDA) tenutosi a Pisa nel 30/11-2/12/2009) [10.1109/ISDA.2009.88].
File in questo prodotto:
File Dimensione Formato  
05364748.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 492.05 kB
Formato Adobe PDF
492.05 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/11381/2363529
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 32
  • ???jsp.display-item.citation.isi??? 23
social impact