A New Approach for In-Vehicle Camera Traffic Sign Detection and Recognition
| Citation: |
Ruta, A.; Porikli, F.; Li, Yongmin,; Watanabe, S.; Kage, H.; Sumi, K., "A New Approach for In-Vehicle Camea Traffic Sign Detection and Recognition", IAPR Conference on Machine vision Applications (MVA), Session 15: Machine Vision for Transportation, May 2009 (MVA 2009) |
| MERL Report: | TR2009-027 |
In this paper we discuss theoretical foundations and a practical realization of a circular traffic sign detection and recognition system operating on board of a vehicle. To initially detect sign candidates in the scene, we utilize the circular Hough transform with an appropriate post-processing in the vote space. Track of an already established candidate is maintained using a function that encodes the relationship between a unique feature representation of the target object and the affine transinformation it is subject to. This function is learned on-the-fly via regression from random distortions applied to the last stable image of the sign. Finally, we adopt a novel AdaBoost algorithm to learn a sign similarity measure from example image pairs labeled either "same" or "different". This enables construction of an efficient multi-class classifier. Prototype implementation has been evaluated on a video captured in crowded street scenes. Good detection and recognition performance was achieved for a 14 class problem which reveals a high potential of our approach.
