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Traffic Sign Recognition using Salient Region Features: A Novel Learning-based Coarse-to-Fine Scheme

Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Anders Ödblom
IEEE Intelligent Vehicles Symposium, June 28-July 1, 2015, Seoul, Korea p. 443-448. (2015)
[Konferensbidrag, refereegranskat]

Traffic sign recognition, including sign detection and classification, is essential for advanced driver assistance systems and autonomous vehicles. This paper introduces a novel machine learning-based sign recognition scheme. In the proposed scheme, detection and classification are realized through learning in a coarse-to-fine manner. Based on the observation that signs in the same category share some common attributes in appearance, the proposed scheme first distinguishes each individual sign category from the background in the coarse learning stage (i.e. sign detection) followed by distinguishing different sign classes within each category in the fine learning stage (i.e. sign classification). Both stages are realized through machine learning techniques. A complete recognition scheme is developed that is effective for simultaneously recognizing multiple categories of traffic signs. In addition, a novel saliency-based feature extraction method is proposed for sign classification. The method segments salient sign regions by leveraging the geodesic energy propagation. Compared with the conventional feature extraction, our method provides more reliable feature extraction from salient sign regions. The proposed scheme is tested and validated on two categories of Chinese traffic signs from Tencent street view. Evaluations on the test dataset show reasonably good performance, with an average of 97.5% true positive and 0.3% false positive on two categories of traffic signs.

Nyckelord: Traffic sign detection and classification, Chinese traffic signs, street view images, salient feature extraction, coarse-to-fine classification.



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Denna post skapades 2015-05-29. Senast ändrad 2016-09-09.
CPL Pubid: 217783

 

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