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Automatic traffic sign recognition based on saliency-enhanced features and SVMs from incrementally built dataset

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Anders Ödblom
Proceedings of the 3rd International Conference on Connected Vehicles and Expo, ICCVE 2014; Vienna; Austria; 3-7 November 2014 p. 947-952. (2014)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Classification, Detection, Saliency feature, Salient region, Traffic sign recognition

Denna post skapades 2016-03-23.
CPL Pubid: 233615


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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)



Chalmers infrastruktur