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Communication Dans Un Congrès Année : 2007

Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system

Résumé

In this paper, we present robust visual speed limit signs detection and recognition systems for American and European signs. Both are variants of the same modular traffic signs recognition architecture, with a sign detection step based only on shape-detection (rectangles or circles), which makes our systems insensitive to color variability and quite robust to illumination variations. Instead of a global recognition, our system classifies (or rejects) the speed-limit sign candidates by segmenting potential digits inside them, and then applying a neural network digit recognition. This helps handling global sign variability, as long as digits are properly recognized. The global sign detection rate is around 90% for both (standard) U.S. and E.U. speed limit signs, with a misclassification rate below 1%, and not a single validated false alarm in >150 minutes of recorded videos. The system processes in real-time videos with images of 640x480 pixels, at ~20frames/s on a standard 2.13GHz dual-core laptop.
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Dates et versions

hal-00422588 , version 1 (08-10-2009)

Identifiants

  • HAL Id : hal-00422588 , version 1

Citer

Fabien Moutarde, Alexandre Bargeton, Anne Herbin, Lowik Chanussot. Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system. IEEE Intelligent Vehicles Symposium (IV'2007), Jun 2007, Istanbul, Turkey. ⟨hal-00422588⟩
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