Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion
conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks
(PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics
theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color
segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left
image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive
image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors
of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained
probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with
recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition
results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile
photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over
92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition.
Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.