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Proceedings Paper

Efficient hybrid monocular-stereo approach to on-board video-based traffic sign detection and tracking
Author(s): Javier Marinas; Luis Salgado; Jon Arróspide; Massimo Camplani
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Paper Abstract

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion.

Paper Details

Date Published: 23 January 2012
PDF: 15 pages
Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 830106 (23 January 2012); doi: 10.1117/12.908585
Show Author Affiliations
Javier Marinas, Univ. Politécnica de Madrid (Spain)
Luis Salgado, Univ. Politécnica de Madrid (Spain)
Jon Arróspide, Univ. Politécnica de Madrid (Spain)
Massimo Camplani, Univ. Politécnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 8301:
Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques
Juha Röning; David P. Casasent, Editor(s)

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