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Proceedings Paper

Multi-resolution model-based traffic sign detection and tracking
Author(s): Javier Marinas; Luis Salgado; Massimo Camplani
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Paper Abstract

In this paper we propose an innovative approach to tackle the problem of traffic sign detection using a computer vision algorithm and taking into account real-time operation constraints, trying to establish intelligent strategies to simplify as much as possible the algorithm complexity and to speed up the process. Firstly, a set of candidates is generated according to a color segmentation stage, followed by a region analysis strategy, where spatial characteristic of previously detected objects are taken into account. Finally, temporal coherence is introduced by means of a tracking scheme, performed using a Kalman filter for each potential candidate. Taking into consideration time constraints, efficiency is achieved two-fold: on the one side, a multi-resolution strategy is adopted for segmentation, where global operation will be applied only to low-resolution images, increasing the resolution to the maximum only when a potential road sign is being tracked. On the other side, we take advantage of the expected spacing between traffic signs. Namely, the tracking of objects of interest allows to generate inhibition areas, which are those ones where no new traffic signs are expected to appear due to the existence of a TS in the neighborhood. The proposed solution has been tested with real sequences in both urban areas and highways, and proved to achieve higher computational efficiency, especially as a result of the multi-resolution approach.

Paper Details

Date Published: 1 May 2012
PDF: 13 pages
Proc. SPIE 8437, Real-Time Image and Video Processing 2012, 84370W (1 May 2012); doi: 10.1117/12.924884
Show Author Affiliations
Javier Marinas, Univ. Politécnica de Madrid (Spain)
Luis Salgado, Univ. Politécnica de Madrid (Spain)
Massimo Camplani, Univ. Politécnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 8437:
Real-Time Image and Video Processing 2012
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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