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AUTOMATIC RECOGNITION OF TRAFFIC SIGNS IN NATURAL SCENE IMAGE
BASED ON CENTRAL PROJECTION TRANSFORMATION
Ka Zhang 3 , Yehua Sheng 3 , Peifang Wang b , Lin Luo c , Chun Ye a , Zhijun Gong d
J Key Laboratory of Virtual Geographic Environment, MOE, Nanjing Normal University, No. 1, Wenyuan Road,
Nanjing, P. R. China, 210046, - zhangka81@126.com, -(shengyehua, yechun)@njnu.edu.cn
b Liuji Secondary School of Wuhe County, Liuji Town, Wuhe County, Anhui Province, P. R. China, 233300,
- wangpfwh@126.com
c Wuxi City Communication Engineering Ltd., No. 888, Taihu East Avenue, Wuxi, Jiangsu Province, P. R. China,
214026, -luolin8398@163.com
d Nanjing Lear Xindi Automotive Interiors System Co, Ltd., No. 376, Hebancun, Maigaoqiao, Nanjing, P. R. China,
210028,-gzj4573@126.com
KEY WORDS: Vision sciences, Photogrammetry, Recognition, Image, Transformation, Neural, Networks
ABSTRACT:
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-bome 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.
1. INTRODUCTION
With the development of society and economy in recent years,
the problem of traffic safety and traffic jam is becoming more
and more serious. Thus the development and application of
intelligent transportation system (ITS) have been attached great
importance by governments and academia. Traffic sign
automatic recognition system, which is an important sub-system
in intelligent transportation system, has become one of the hot
spots in the field of ITS, and it’s also a very difficult problem in
real scene image recognition. How to detect traffic signs from
complex real scene images rapidly and efficiently is the key
step for traffic sign automatic recognition. Colour and shape
features are usually used to detect traffic signs. The method of
edge extraction and shape analysis is a common one for traffic
sign detection (Loy, et al, 2004; Alefs, et al, 2007). In addition,
the method based on combination of colour and shape features
is another common one for traffic sign detection. In this kind of
method, colour segmentation is firstly used to eliminate
background-objects and obtain binary image. Then shape
analysis is used to detect traffic sign regions in the segmented
binary image (Gao, et al, 2006; Filipe, et al, 2007; Zhang, et al,
Once candidate regions of traffic signs are detected from the
image by colour segmentation or edge detection, traffic signs
can be recognized according to their shape features. Hsu firstly
used template images in database to train matching pursuit (MP)
filters to find a set of best MP filter bases of each road sign,
then used trained MP filter bases to find the best match between
detected road sign and template sign generated by MP filter
bases(Hsu, et al, 2001). Perez firstly carried out Fourier
transformation to input image and template image, then
computed similarity measure between them by nonlinear
correlator for road sign recognition (Perez, et al, 2002).
Escalera carried out normalization processing and binary
processing on detected road signs first, then input binary image
to the adaptive resonance theory paradigm neural network for
road sign recognition (Escalera, et al, 2003). Fang presented an
automatic road sign detection and recognition system based on
a computational model of human visual recognition processing
(Fang, et al, 2004). Paclik proposed a novel concept of a
trainable similarity measure, which can alleviate some
shortcomings of traditional cross correlation similarity. He used
this new similarity measure to recognize prohibition signs
(Paclik, et al, 2006). Maldonado-Bascon used DtB, which is
distance vector composed of distances between edges of road
signs and borders of sign candidate regions, as the shape feature
of road sign, then input the feature to the trained support vector
machines for road sign recognition (Maldonado-Bascon, et al,
2007). Apart from the above methods, many methods based on
template matching, neural networks and genetic algorithm are
used for traffic sign recognition (Miura, et al, 2000; Liu, et al,
2002; Gil-Jimenez, et al, 2007; Kuo, et al, 2007; etc.).
However, the main shortcoming of above methods is that these
methods need the same size between detected real sign and
reference sign. Detected sign regions must be normalized to the
uniform size, which will damage the original information of
detected signs and influence the recognition rate. In this paper,
