Geodesic Distance Transform-based Salient Region Segmentation for Automatic Traffic Sign Recognition
Paper i proceeding, 2016

Visual-based traffic sign recognition (TSR) requires first detecting and then classifying signs from captured images. In such a cascade system, classification accuracy is often affected by the detection results. This paper proposes a method for extracting a salient region of traffic sign within a detection window for more accurate sign representation and feature extraction, hence enhancing the performance of classification. In the proposed method, a superpixel-based distance map is firstly generated by applying a signed geodesic distance transform from a set of selected foreground and background seeds. An effective method for obtaining a final segmentation from the distance map is then proposed by incorporating the shape constraints of signs. Using these two steps, our method is able to automatically extract salient sign regions of different shapes. The proposed method is tested and validated in a complete TSR system. Test results show that the proposed method has led to a high classification accuracy (97.11%) on a large dataset containing street images. Comparing to the same TSR system without using saliency-segmented regions, the proposed method has yielded a marked performance improvement (about 12.84%). Future work will be on extending to more traffic sign categories and comparing with other benchmark methods.

traffic sign detection

traffic sign classification

geodesic distance transform

traffic sign segmentation

driving assistance.

salient regions

autonomous driving


Keren Fu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Anders Ödblom

Volvo Cars

Feng Liu

Volvo Cars

Proceedings - 2016 IEEE Intelligent Vehicles Symposium, IV 2016, Gotenburg, Sweden, 19-22 June 2016

978-1-5090-1821-5 (ISBN)


Informations- och kommunikationsteknik




Datorseende och robotik (autonoma system)





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