Automatic traffic sign recognition based on saliency-enhanced features and SVMs from incrementally built dataset
Paper i proceeding, 2014

This paper proposes an automatic traffic sign recognition method based on saliency-enhanced feature and SVMs. As when human observe a traffic sign, a two-stage procedure is performed by first locating the region of sign according to its unique shape and color, and second paying attention to content inside the sign. The proposed saliency feature extraction attempts to resemble these two processing stages. We model the first stage via extracting salient regions of signs from detected bounding boxes contributed by sign detector. Salient region extraction is formed as an energy propagation process on local structured graph. The second stage is modeled by exploiting a non-linear color mapping under the guidance of the output of the first stage. As results, salient signature inside a sign is popped up and can be directly used by subsequent SVMs for classification. The proposed method is validated on Chinese traffic sign dataset that is incrementally built.

Classification

Salient region

Saliency feature

Traffic sign recognition

Detection

Författare

Keren Fu

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Anders Ödblom

Volvo

Proceedings of the 3rd International Conference on Connected Vehicles and Expo, ICCVE 2014; Vienna; Austria; 3-7 November 2014

947-952

Ämneskategorier

Reglerteknik

DOI

10.1109/ICCVE.2014.7297698

ISBN

9781479967292