A Fast Sign Localization System Using Discriminative Color Invariant Segmentation
Building intelligent traffic guide systems has been an
interesting subject recently. A good system should be able to observe
all important visual information to be able to analyze the context of
the scene. To do so, signs in general, and traffic signs in particular,
are usually taken into account as they contain rich information to
these systems. Therefore, many researchers have put an effort on
sign recognition field. Sign localization or sign detection is the most
important step in the sign recognition process. This step filters out
non informative area in the scene, and locates candidates in later
steps. In this paper, we apply a new approach in detecting sign
locations using a new color invariant model. Experiments are carried
out with different datasets introduced in other works where authors
claimed the difficulty in detecting signs under unfavorable imaging
conditions. Our method is simple, fast and most importantly it gives
a high detection rate in locating signs.
[1] C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, and T. Koehler. A system
for traffic sign detection, tracking, and recognition using color, shape, and
motion information. In Proceedings of the IEEE conference on Intelligent
Vehicles Symposium, pages 255-260, 2005.
[2] Y.B. Damavandi and K. Mohammadi. Speed limit traffic sign detection
and recognition. In IEEE conference on Cybernetics and Intelligent
Systems, pages 797-802, 2004.
[3] M.A. Garcia-Garrido, M.A. Sotelo, and E. Martin-Gorostiza. Fast road
sign detection using hough transform for assisted driving of road vehicles.
In R.M. Diaz and et al., editors, EUROCAST, LNCS 3643, pages 543-
548, 2005.
[4] J.M. Geusbroek, R.v.d. Boomgaard, and A.W.M. Smeulders. Color invariance.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
23(12):1338-1350, 2001.
[5] T. Gevers and A.W.M. Smeulders. Color based object recognition. Pattern
Recognition, 32:453-464, 1999.
[6] T. Gevers, J. Weijer, and H. Stokman. Color feature detection. Color
Image Processing: Methods and Applications, editors R. Lukac and K.N.
Plataniotis, CRC Press, 2006.
[7] C. Grigorescu and N. Petkov. Distance sets for shape filters and shape
recognition. IEEE Transactions on Image Processing, 12(10):1274-1286,
2003.
[8] Y. Liu, S. Goto, and T. Ikenaga. An MRF model based algorithm of
triangular shape object detection in color images. International Journal
of Information Technology, 12(2):55-65, 2006.
[9] L.D. Lopez and O. Fuentes. Color-based road sign detection and tracking.
In M. Kamel, A. Campilho, and et al., editors, ICIAR, LNCS 4633, pages
1138-1147, 2007.
[10] J.T. Oh, H.W. Kwak, Y.H. Sohn, and W.H. Kim. Segmentation and
recognition of traffic sign using shape information. In G. Bebis and
et al., editors, ISVC, LNCS 3804, pages 519-526, 2005.
[11] L. Sekanina and J. Torresen. Detection of norwegian speed limit signs.
In Proceedings of the 16th European Simulation Multiconference on
Modelling and Simulation, pages 337-340, 2002.
[12] W.G. Shadeed, D.I. Abu-Al-Nadi, and M.J. Mismar. Road traffic sign
detection in color images. In ICECS, 2003.
[13] P. Silapachote, J. Qeinman, A. Hanson, R. Weiss, and M.A. Mattar.
Automatic sign detection and recognition in natural scenes. In J. Coughlan
and R. Manduchi, editors, IEEE workshop on Computer Vision Applications
for the Visually Impaired, 2005.
[14] G. Wu, W. Liu, X. Xie, and Q. Wei. A shape detection method based on
the radial symmetry nature and direction discriminated voting. In ICIP,
2007.
[15] W.Wu, X. Chen, and J. Yang. Detection of text on road signs from video.
IEEE Transactions on Intelligent Transportation Systems, 6(4):378-390,
2005.
[16] H.M. Yang, C.L. Liu, K.H. Liu, and S.M. Huang. Traffic sign recognition
in disturbing environments. In N. Zhong and et al., editors, ISMIS, LNAI
2871, pages 252-261, 2003.
[17] Hsiu-Ming Yang. Traffic dataset in disturbing environment
http://www.cs.nccu.edu.tw/~chaolin/papers/ismis03/testdata.html.
[1] C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, and T. Koehler. A system
for traffic sign detection, tracking, and recognition using color, shape, and
motion information. In Proceedings of the IEEE conference on Intelligent
Vehicles Symposium, pages 255-260, 2005.
[2] Y.B. Damavandi and K. Mohammadi. Speed limit traffic sign detection
and recognition. In IEEE conference on Cybernetics and Intelligent
Systems, pages 797-802, 2004.
[3] M.A. Garcia-Garrido, M.A. Sotelo, and E. Martin-Gorostiza. Fast road
sign detection using hough transform for assisted driving of road vehicles.
In R.M. Diaz and et al., editors, EUROCAST, LNCS 3643, pages 543-
548, 2005.
[4] J.M. Geusbroek, R.v.d. Boomgaard, and A.W.M. Smeulders. Color invariance.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
23(12):1338-1350, 2001.
[5] T. Gevers and A.W.M. Smeulders. Color based object recognition. Pattern
Recognition, 32:453-464, 1999.
[6] T. Gevers, J. Weijer, and H. Stokman. Color feature detection. Color
Image Processing: Methods and Applications, editors R. Lukac and K.N.
Plataniotis, CRC Press, 2006.
[7] C. Grigorescu and N. Petkov. Distance sets for shape filters and shape
recognition. IEEE Transactions on Image Processing, 12(10):1274-1286,
2003.
[8] Y. Liu, S. Goto, and T. Ikenaga. An MRF model based algorithm of
triangular shape object detection in color images. International Journal
of Information Technology, 12(2):55-65, 2006.
[9] L.D. Lopez and O. Fuentes. Color-based road sign detection and tracking.
In M. Kamel, A. Campilho, and et al., editors, ICIAR, LNCS 4633, pages
1138-1147, 2007.
[10] J.T. Oh, H.W. Kwak, Y.H. Sohn, and W.H. Kim. Segmentation and
recognition of traffic sign using shape information. In G. Bebis and
et al., editors, ISVC, LNCS 3804, pages 519-526, 2005.
[11] L. Sekanina and J. Torresen. Detection of norwegian speed limit signs.
In Proceedings of the 16th European Simulation Multiconference on
Modelling and Simulation, pages 337-340, 2002.
[12] W.G. Shadeed, D.I. Abu-Al-Nadi, and M.J. Mismar. Road traffic sign
detection in color images. In ICECS, 2003.
[13] P. Silapachote, J. Qeinman, A. Hanson, R. Weiss, and M.A. Mattar.
Automatic sign detection and recognition in natural scenes. In J. Coughlan
and R. Manduchi, editors, IEEE workshop on Computer Vision Applications
for the Visually Impaired, 2005.
[14] G. Wu, W. Liu, X. Xie, and Q. Wei. A shape detection method based on
the radial symmetry nature and direction discriminated voting. In ICIP,
2007.
[15] W.Wu, X. Chen, and J. Yang. Detection of text on road signs from video.
IEEE Transactions on Intelligent Transportation Systems, 6(4):378-390,
2005.
[16] H.M. Yang, C.L. Liu, K.H. Liu, and S.M. Huang. Traffic sign recognition
in disturbing environments. In N. Zhong and et al., editors, ISMIS, LNAI
2871, pages 252-261, 2003.
[17] Hsiu-Ming Yang. Traffic dataset in disturbing environment
http://www.cs.nccu.edu.tw/~chaolin/papers/ismis03/testdata.html.
@article{"International Journal of Information, Control and Computer Sciences:56009", author = "G.P. Nguyen and H.J. Andersen", title = "A Fast Sign Localization System Using Discriminative Color Invariant Segmentation", abstract = "Building intelligent traffic guide systems has been an
interesting subject recently. A good system should be able to observe
all important visual information to be able to analyze the context of
the scene. To do so, signs in general, and traffic signs in particular,
are usually taken into account as they contain rich information to
these systems. Therefore, many researchers have put an effort on
sign recognition field. Sign localization or sign detection is the most
important step in the sign recognition process. This step filters out
non informative area in the scene, and locates candidates in later
steps. In this paper, we apply a new approach in detecting sign
locations using a new color invariant model. Experiments are carried
out with different datasets introduced in other works where authors
claimed the difficulty in detecting signs under unfavorable imaging
conditions. Our method is simple, fast and most importantly it gives
a high detection rate in locating signs.", keywords = "Sign localization, color-based segmentation.", volume = "2", number = "10", pages = "3384-7", }
