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Proceedings Paper

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks
Author(s): Yehua Sheng; Ka Zhang; Chun Ye; Cheng Liang; Jian Li
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Paper Abstract

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.

Paper Details

Date Published: 25 April 2008
PDF: 12 pages
Proc. SPIE 7000, Optical and Digital Image Processing, 70001I (25 April 2008); doi: 10.1117/12.780418
Show Author Affiliations
Yehua Sheng, Nanjing Normal Univ. (China)
Ka Zhang, Nanjing Normal Univ. (China)
Chun Ye, Nanjing Normal Univ. (China)
Cheng Liang, Nanjing Normal Univ. (China)
Jian Li, Nanjing Normal Univ. (China)

Published in SPIE Proceedings Vol. 7000:
Optical and Digital Image Processing
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal; Frédéric Truchetet, Editor(s)

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