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Traffic Sign Detection Using a Cascade Method With Fast Feature Extraction and Saliency Test

Lookup NU author(s): Dr Jiawei Xu

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2017.

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Abstract

Automatic traffic sign detection is challenging due to the complexity of scene images, and fast detection is required in real applications such as driver assistance systems. In this paper, we propose a fast traffic sign detection method based on a cascade method with saliency test and neighboring scale awareness. In the cascade method, feature maps of several channels are extracted efficiently using approximation techniques. Sliding windows are pruned hierarchically using coarse-to-fine classifiers and the correlation between neighboring scales. The cascade system has only one free parameter, while the multiple thresholds are selected by a data-driven approach. To further increase speed, we also use a novel saliency test based on mid-level features to pre-prune background windows. Experiments on two public traffic sign data sets show that the proposed method achieves competing performance and runs 2~7 times as fast as most of the state-of-the-art methods.

Publication metadata

Author(s): Wang D, Hou X, Xu J, Yue S, Liu C

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Intelligent Transportation Systems

Year: 2017

Volume: 18

Issue: 12

Pages: 3290-3302

Print publication date: 01/12/2017

Online publication date: 04/04/2017

Acceptance date: 09/03/2017

Date deposited: 15/03/2018

ISSN (print): 1524-9050

ISSN (electronic): 1558-0016

Publisher: IEEE

URL: https://doi.org/10.1109/TITS.2017.2682181

DOI: 10.1109/TITS.2017.2682181

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