Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information

H; ri Santoso; Kazuo Nakamura

doi:10.20965/jaciii.2007.p0491

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JACIII Vol.11 No.5 pp. 491-501

doi: 10.20965/jaciii.2007.p0491

(2007)

Paper:

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Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information

Handri Santoso^* and Kazuo Nakamura^**

^*Graduate School of Information Science and Control Engineering, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, Japan

^**Department of Management and Information Systems Science, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, Japan

Received:

December 6, 2006

Accepted:

April 25, 2007

Published:

June 20, 2007

Keywords:

slippery road, sidewalk surface condition, background image, image texture, meteorological information

Abstract

Slippery roads, especially during and after a heavy snow fall, may lead to accidents causing injuries and fatalities to vulnerable person such as the aged. In this context, it is important to keep pedestrian aware of sidewalk condition. This paper aims at proposing detection of several sidewalk conditions under different environment circumstance. At the front end, image and video processing is perrformed to separate background and foreground images. Background image features are extracted using several texture feature generators. In this study, factor analysis methods are employed to examine the pattern of correlations among variables, and to reduce data dimensionality. Finally, Artificial Neural Network is employed to discriminate sidewalk surface condition, i.e., dry, wet, or snow.

Cite this article as:

H. Santoso and K. Nakamura, “Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.5, pp. 491-501, 2007.

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References

[1] Pedestrian and Bicycle Information Center,
http://www.walkinginfo.org.
[2] C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Transaction on Pattern Analysis & Machine Intelligence, Vol.22 (8), pp. 747-757, 2000.
[3] M. Yamada, K. Ueda, I. Horiba, S. Yamamoto, and S. Tsugawa, “Detection of Wet-Road Condition from Images Captured by A Vehicle-Mounted Camera,” Journal of Robotics and Mechatronics, Vol.17, No.3, pp. 269-276, 2005.
[4] T. Kuno and H. Sugiura, “Detection of Road Condition with CCD Cameras Mounted on a Vehicle,” System and Computers in Japan, Vol.30 (14), 1999.
[5] J. A. Bilmes, “A Gentle Tutorial of the EM algorithm and its application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,” ICSI-Technical Report-97-021, 1997.
[6] S. Theodoridis and K. Koutroumbas, “Pattern Recognition,” 3^rd edition, Academic Press, 2006.
[7] M. Yamada, K. Ueda, I. Horiba, and N. Sugie, “Discrimination of the Road Condition toward Understanding of Vehicle Driving Environment,” IEEE Transaction on ITS, Vol.2 (1), pp. 26-31, 2001.
[8] P. Kline, “An Easy Guide to Factor Analysis,” Routledge, 1994.
[9] J. Kim and C. W. Mueller, “Factor Analysis: Statistical Methods and Practical Issues,” Sage Publication, Inc, 1978.
[10] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” 2^nd edition, Wiley-Interscience, 2000.
[11] M. I. Jordan, “Why the logistic function? A tutorial discussion on probabilities and neural networks,” MIT Computational Cognitive Science, technical report, 1995.
[12] Japan Meteorological Agency,
http://www.data.kishou.go.jp/etrn/index.html

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Pedestrian and Bicycle Information Center,
http://www.walkinginfo.org.

[2] [2] C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Transaction on Pattern Analysis & Machine Intelligence, Vol.22 (8), pp. 747-757, 2000.

[3] [3] M. Yamada, K. Ueda, I. Horiba, S. Yamamoto, and S. Tsugawa, “Detection of Wet-Road Condition from Images Captured by A Vehicle-Mounted Camera,” Journal of Robotics and Mechatronics, Vol.17, No.3, pp. 269-276, 2005.

[4] [4] T. Kuno and H. Sugiura, “Detection of Road Condition with CCD Cameras Mounted on a Vehicle,” System and Computers in Japan, Vol.30 (14), 1999.

[5] [5] J. A. Bilmes, “A Gentle Tutorial of the EM algorithm and its application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,” ICSI-Technical Report-97-021, 1997.

[6] [6] S. Theodoridis and K. Koutroumbas, “Pattern Recognition,” 3^rd edition, Academic Press, 2006.

[7] [7] M. Yamada, K. Ueda, I. Horiba, and N. Sugie, “Discrimination of the Road Condition toward Understanding of Vehicle Driving Environment,” IEEE Transaction on ITS, Vol.2 (1), pp. 26-31, 2001.

[8] [8] P. Kline, “An Easy Guide to Factor Analysis,” Routledge, 1994.

[9] [9] J. Kim and C. W. Mueller, “Factor Analysis: Statistical Methods and Practical Issues,” Sage Publication, Inc, 1978.

[10] [10] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” 2^nd edition, Wiley-Interscience, 2000.

[11] [11] M. I. Jordan, “Why the logistic function? A tutorial discussion on probabilities and neural networks,” MIT Computational Cognitive Science, technical report, 1995.

[12] [12] Japan Meteorological Agency,
http://www.data.kishou.go.jp/etrn/index.html

Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information

Handri Santoso* and Kazuo Nakamura**

Handri Santoso^* and Kazuo Nakamura^**