Automatic Recognition of Traffic Signs in Natural Scene Image Based on Central Projection Transformation

Considering the problem of automatic traffic signs recognition in natural scene image (mainly including three kinds of traffic signs: yellow warning signs, red prohibition signs and blue mandatory signs), a new method for traffic signs recognition based on central projection transformation is proposed in this paper. In this method, self-adaptive image segmentation is firstly used to extract binary inner images of detected traffic signs after they are detected from natural scene images. Secondly, one-dimensional feature vectors of inner images are computed by central projection transformation. Lastly, these vectors are input to the trained probabilistic neural networks (PNN) for exact classification, the output of PNN is final recognition result. The new method is applied to 221 natural scene images taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a recognition rate of over 98%. Especially, the problem of confirming optimal projection number in central projection transformation is solved by the information entropy in this paper. Moreover, the proposed recognition method is compared with other recognition methods based on three kinds of invariant moments. Results of contrastive experiments also show that the method proposed in this paper is effective and reliable.