Road safety is influenced by the accurate placement and visibility of road signs, which are maintained based on inventories of traffic signs. These inventories are created (semi-)automatically from street-level images, based on object detection and classification. These systems often neglect the present complimentary signs (subsigns), although clearly important for the meaning and validity of signs. This paper presents a generic, learning-based approach for both detection and classification of subsigns, which is based on the same principles as the system employed for finding traffic signs and can be used as an extension to automated inventory systems. The system starts with detection of subsigns in a region below each detected sign, followed by analysis of the results obtained for all capturings of the same sign. When a subsign is found, the corresponding pixel regions are extracted and subject to classification. This recognition system is evaluated on 3;104 signs (397 with subsign) identified by an existing inventory system. At a detection rate of 98%, only 757 signs (24:4% of the signs) are labeled as containing a subsign, while 91:4% of the subsigns of a class known to our classifier are also classified correctly.
|Title of host publication||Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014), 5-8 January 2014, Lisbon, Portugal|
|Publication status||Published - 2014|