© The Institution of Engineering and Technology
This study's purpose is to introduce eigen-based traffic sign recognition. This technique is based on invoking the principal component analysis (PCA) algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign's borders and speed limit pictograms that were extracted automatically from real-world images. A classification rate of 96.8 and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs' outer shapes were used and for rotated speed limit pictograms the result was 80%.
References
-
-
1)
-
Buluswar, S., Draper, B.: `Color recognition in outdoor images', Int. Conf. on Computer Vision, 1998, Bombay, India, p. 171–177.
-
2)
-
C.F. Paulo ,
P.L. Correia
.
Traffic sign recognition based on pictogram contours.
Ninth Int. Workshop on Image Analysis for Multimedia Interactive Services
,
67 -
70
-
3)
-
Fleyeh, H., Dougherty, M.: `SVM based traffic sign classification using legendre moments', Third Indian Int. Conf. on Artificial Intelligence (IICAI-07), 2007, Pune, India.
-
4)
-
Fleyeh H.: Traffic Signs Database, 2009.
-
5)
-
S. Bascon ,
J. Rodrigues ,
S. Arroyo ,
A. Caballero ,
F. Ferreras
.
An optimization on pictogram identification for road-sign recognition task using SVMs.
Comput. Vis. Image Underst.
,
373 -
383
-
6)
-
A. Ruta ,
Y. Li ,
X. Liu
.
Real-time traffic sign recognition from video by class-specific discriminative features.
Pattern Recognit.
,
416 -
430
-
7)
-
Vitabile, S., Gentile, A., Sorbello, F.: `A neural network based automatic road sign recognizer', The 2002 Int. Joint Conf. on Neural Networks, 2002, Honolulu, HI, USA, p. 2315–2320.
-
8)
-
Hoferlin, B., Zimmermann, K.: `Towards reliable traffic sign recognition', 2009 IV Symp., 2009, p. 324–329.
-
9)
-
Vitabile, S., Pollaccia, G., Pilato, G., Sorbello, F.: `Road sign recognition using a dynamic pixel aggregation technique in the HSV color space', 11thInt. Conf. on Image Analysis and Processing, 2001, Palermo, Italy, p. 572–577.
-
10)
-
Fleyeh, H.: `Shadow and highlight invariant colour segmentation algorithm for traffic signs', 2006 IEEE Conf. on Cybernetics and Intelligent Systems, 2006, Bangkok, Thailand, p. 108–114.
-
11)
-
I. Joliffe
.
(1986)
Principal component analysis.
-
12)
-
Miura, J., Kanda, T., Shirai, Y.: `An active vision system for real-time traffic sign recognition', 2000 IEEE Intelligent Transportation Systems, 2000, Dearborn, MI, USA, p. 52–57.
-
13)
-
L. Sirovich ,
M. Kirby
.
Low-dimensional procedure for the characterization of human faces.
J. Opt. Soc. Am. A
,
519 -
524
-
14)
-
W. Liu ,
K. Maruya
.
Detection and recognition of traffic signs in adverse conditions.
2009 Intelligent Vehicle Symp.
,
335 -
340
-
15)
-
Fang, C., Fuh, C., Chen, S., Yen, P.: `A road sign recognition system based on dynamic visual model', The 2003 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2003, Madison, WI, p. 750–755.
-
16)
-
M. Turk ,
A. Pentland
.
Eigenfaces for recognition.
J. Cogn. Neurosci.
,
71 -
86
-
17)
-
Fleyeh, H.: `Traffic sign recognition by fuzzy sets', 2008 IEEE Intelligent Vehicles Symp., 2008, Eindhoven, Netherlands, p. 422–427.
-
18)
-
K. Pearson
.
On lines and planes of closest fit to systems of points in space.
Philos. Mag.
,
559 -
572
-
19)
-
Fleyeh, H., Dougherty, M.: `Traffic sign classification using invariant features and support vector machines', 2008 IEEE Intelligent Vehicles Symp., 2008, Eindhoven, Netherlands, p. 530–535.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2010.0159
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