Abstract

Traffic sign recognition is one of the main components of intelligent transportation systems (ITS). It improves safety by informing the driver of the current state of the road, e.g., warnings, prohibitions, restrictions, and other information useful for driving. This paper presents a new road sign recognition method that is achieved in three main steps. The first step maps the input image from the Cartesian coordinate system to the log-polar one. The second step computes the histogram of oriented gradients, local binary pattern, and local self-similarity characteristics from the image represented in the log-polar coordinate system. The third step performs classification on the basis of the random forest classifier and the features computed in the second step. The proposed method has been tested on the German Traffic Sign Recognition Benchmark dataset, and the results obtained are satisfactory when compared to the state-of-the-art approaches.

© 2018 Optical Society of America

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References

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2018 (1)

S. Charfi and M. El Ansari, “Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images,” Multimedia Tools Appl. 77, 4047–4064 (2018).
[Crossref]

2017 (2)

A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

A. Ellahyani and M. El Ansari, “Mean shift and log-polar transform for road sign detection,” Multimedia Tools Appl. 76, 24495–24513 (2017).
[Crossref]

2016 (5)

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

A. Ellahyani, M. El Ansari, and I. El Jaafari, “Traffic sign detection and recognition based on random forests,” Appl. Soft Comput. 46, 805–815 (2016).
[Crossref]

F. Takarli, A. Aghagolzadeh, and H. Seyedarabi, “Combination of high-level features with low-level features for detection of pedestrian,” Signal Image Video Process. 10, 93–101 (2016).
[Crossref]

A. Markman, A. Carnicer, and B. Javidi, “Security authentication with a three-dimensional optical phase code using random forest classifier,” J. Opt. Soc. Am. A 33, 1160–1165 (2016).
[Crossref]

2015 (1)

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
[Crossref]

2014 (6)

J. Jin, K. Fu, and C. Zhang, “Traffic sign recognition with hinge loss trained convolutional neural networks,” IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014).
[Crossref]

K. Yang, E. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal Image Video Process. 8, 135–144 (2014).
[Crossref]

Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG-ALBP feature for pedestrian detection,” Signal Image Video Process. 8, 125–134 (2014).
[Crossref]

H. Liu, Y. Liu, and F. Sun, “Traffic sign recognition using group sparse coding,” Inf. Sci. 266, 75–89 (2014).
[Crossref]

Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
[Crossref]

C. Cusano, P. Napoletano, and R. Schettini, “Combining local binary patterns and local color contrast for texture classification under varying illumination,” J. Opt. Soc. Am. A 31, 1453–1461 (2014).
[Crossref]

2013 (1)

X. Yang, “Enhancement for road sign images and its performance evaluation,” Optik 124, 1957–1960 (2013).
[Crossref]

2012 (2)

F. Zaklouta and B. Stanciulescu, “Real-time traffic-sign recognition using tree classifiers,” IEEE Trans. Intell. Transp. Syst. 13, 1507–1514 (2012).
[Crossref]

K. Lu, Z. Ding, and S. Ge, “Sparse-representation-based graph embedding for traffic sign recognition,” IEEE Trans. Intell. Transp. Syst. 13, 1515–1524 (2012).
[Crossref]

2011 (2)

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
[Crossref]

H. Fleyeh and E. Davami, “Eigen-based traffic sign recognition,” IET Intell. Transp. Syst. 5, 190–196 (2011).
[Crossref]

2001 (1)

L. Breiman, “Random forests,” Mach. Learn. 45, 5–32 (2001).
[Crossref]

1996 (1)

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition 29, 51–59 (1996).
[Crossref]

Abdelouahad, A. A.

M. Souaidi, A. A. Abdelouahad, and M. El Ansari, “A fully automated ulcer detection system for wireless capsule endoscopy images,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

Acharya, U. R.

A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

Aghagolzadeh, A.

F. Takarli, A. Aghagolzadeh, and H. Seyedarabi, “Combination of high-level features with low-level features for detection of pedestrian,” Signal Image Video Process. 10, 93–101 (2016).
[Crossref]

Ang, L. M.

K. H. Lim, K. P. Seng, and L. M. Ang, “Intra color-shape classification for traffic sign recognition,” in International Computer Symposium (ICS) (IEEE, 2010), pp. 642–647.

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn. 45, 5–32 (2001).
[Crossref]

Carnicer, A.

Charfi, S.

S. Charfi and M. El Ansari, “Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images,” Multimedia Tools Appl. 77, 4047–4064 (2018).
[Crossref]

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

M. El Ansari and S. Charfi, “Computer-aided system for polyp detection in wireless capsule endoscopy images,” in International Conference on Wireless Networks and Mobile Communications (WINCOM) (IEEE, 2017), pp. 1–6.

Chen, H.-J.

C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

Chokkadi, S.

A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

Christiannini, N.

N. Christiannini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods (2000).

Ciresan, D.

D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 1918–1921.

Cusano, C.

Dalal, N.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2005), Vol. 1, pp. 886–893.

Davami, E.

H. Fleyeh and E. Davami, “Eigen-based traffic sign recognition,” IET Intell. Transp. Syst. 5, 190–196 (2011).
[Crossref]

Delp, E.

K. Yang, E. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal Image Video Process. 8, 135–144 (2014).
[Crossref]

Di Stefano, L.

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
[Crossref]

Ding, Z.

K. Lu, Z. Ding, and S. Ge, “Sparse-representation-based graph embedding for traffic sign recognition,” IEEE Trans. Intell. Transp. Syst. 13, 1515–1524 (2012).
[Crossref]

Du, E.

K. Yang, E. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal Image Video Process. 8, 135–144 (2014).
[Crossref]

Duan, B.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

El Ansari, M.

S. Charfi and M. El Ansari, “Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images,” Multimedia Tools Appl. 77, 4047–4064 (2018).
[Crossref]

A. Ellahyani and M. El Ansari, “Mean shift and log-polar transform for road sign detection,” Multimedia Tools Appl. 76, 24495–24513 (2017).
[Crossref]

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

A. Ellahyani, M. El Ansari, and I. El Jaafari, “Traffic sign detection and recognition based on random forests,” Appl. Soft Comput. 46, 805–815 (2016).
[Crossref]

A. Ellahyani and M. El Ansari, “A new designed descriptor for road sign recognition,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

A. Ellahyani and M. El Ansari, “Complementary features for traffic sign detection and recognition,” in IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) (IEEE, 2016), pp. 1–6.

M. Souaidi, A. A. Abdelouahad, and M. El Ansari, “A fully automated ulcer detection system for wireless capsule endoscopy images,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

M. El Ansari, R. Lahmyed, and A. Trémeau, “A hybrid pedestrian detection system based on visible images and lidar data,” in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (2018), Vol. 5, pp. 27–29.

M. El Ansari and S. Charfi, “Computer-aided system for polyp detection in wireless capsule endoscopy images,” in International Conference on Wireless Networks and Mobile Communications (WINCOM) (IEEE, 2017), pp. 1–6.

El Jaafari, I.

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

A. Ellahyani, M. El Ansari, and I. El Jaafari, “Traffic sign detection and recognition based on random forests,” Appl. Soft Comput. 46, 805–815 (2016).
[Crossref]

Ellahyani, A.

A. Ellahyani and M. El Ansari, “Mean shift and log-polar transform for road sign detection,” Multimedia Tools Appl. 76, 24495–24513 (2017).
[Crossref]

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

A. Ellahyani, M. El Ansari, and I. El Jaafari, “Traffic sign detection and recognition based on random forests,” Appl. Soft Comput. 46, 805–815 (2016).
[Crossref]

A. Ellahyani and M. El Ansari, “Complementary features for traffic sign detection and recognition,” in IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) (IEEE, 2016), pp. 1–6.

A. Ellahyani and M. El Ansari, “A new designed descriptor for road sign recognition,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

Fioraio, N.

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
[Crossref]

Fleyeh, H.

H. Fleyeh and E. Davami, “Eigen-based traffic sign recognition,” IET Intell. Transp. Syst. 5, 190–196 (2011).
[Crossref]

Fu, K.

J. Jin, K. Fu, and C. Zhang, “Traffic sign recognition with hinge loss trained convolutional neural networks,” IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014).
[Crossref]

Gao, H.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

Ge, S.

K. Lu, Z. Ding, and S. Ge, “Sparse-representation-based graph embedding for traffic sign recognition,” IEEE Trans. Intell. Transp. Syst. 13, 1515–1524 (2012).
[Crossref]

Görmer, S. M.

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
[Crossref]

Gudigar, A.

A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

Hao, X.-L.

C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

Harwood, D.

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition 29, 51–59 (1996).
[Crossref]

Huang, Y.

Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG-ALBP feature for pedestrian detection,” Signal Image Video Process. 8, 125–134 (2014).
[Crossref]

Irani, M.

E. Shechtman and M. Irani, “Matching local self-similarities across images and videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2007), pp. 1–8.

Javidi, B.

Jin, J.

J. Jin, K. Fu, and C. Zhang, “Traffic sign recognition with hinge loss trained convolutional neural networks,” IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014).
[Crossref]

Koutti, L.

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

Kummert, A.

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
[Crossref]

Lahmyed, R.

M. El Ansari, R. Lahmyed, and A. Trémeau, “A hybrid pedestrian detection system based on visible images and lidar data,” in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (2018), Vol. 5, pp. 27–29.

Lau, W.-S.

Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
[Crossref]

LeCun, Y.

P. Sermanet and Y. LeCun, “Traffic sign recognition with multi-scale convolutional networks,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 2809–2813.

Lim, K. H.

K. H. Lim, K. P. Seng, and L. M. Ang, “Intra color-shape classification for traffic sign recognition,” in International Computer Symposium (ICS) (IEEE, 2010), pp. 642–647.

Liu, H.

H. Liu, Y. Liu, and F. Sun, “Traffic sign recognition using group sparse coding,” Inf. Sci. 266, 75–89 (2014).
[Crossref]

Liu, W.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

Liu, Y.

H. Liu, Y. Liu, and F. Sun, “Traffic sign recognition using group sparse coding,” Inf. Sci. 266, 75–89 (2014).
[Crossref]

Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG-ALBP feature for pedestrian detection,” Signal Image Video Process. 8, 125–134 (2014).
[Crossref]

Lu, K.

K. Lu, Z. Ding, and S. Ge, “Sparse-representation-based graph embedding for traffic sign recognition,” IEEE Trans. Intell. Transp. Syst. 13, 1515–1524 (2012).
[Crossref]

Lv, J.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

Markman, A.

Masci, J.

D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 1918–1921.

Mazoul, A.

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

Meier, U.

D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 1918–1921.

Meuter, M.

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
[Crossref]

Müller-Schneiders, S.

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
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M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
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T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition 29, 51–59 (1996).
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S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
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T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition 29, 51–59 (1996).
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A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

Salti, S.

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
[Crossref]

Schettini, R.

Schmidhuber, J.

D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 1918–1921.

Seet, G.

Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
[Crossref]

Seng, K. P.

K. H. Lim, K. P. Seng, and L. M. Ang, “Intra color-shape classification for traffic sign recognition,” in International Computer Symposium (ICS) (IEEE, 2010), pp. 642–647.

Sermanet, P.

P. Sermanet and Y. LeCun, “Traffic sign recognition with multi-scale convolutional networks,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 2809–2813.

Seyedarabi, H.

F. Takarli, A. Aghagolzadeh, and H. Seyedarabi, “Combination of high-level features with low-level features for detection of pedestrian,” Signal Image Video Process. 10, 93–101 (2016).
[Crossref]

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N. Christiannini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods (2000).

Shechtman, E.

E. Shechtman and M. Irani, “Matching local self-similarities across images and videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2007), pp. 1–8.

Shen, Y.

C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

Souaidi, M.

M. Souaidi, A. A. Abdelouahad, and M. El Ansari, “A fully automated ulcer detection system for wireless capsule endoscopy images,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

Stanciulescu, B.

F. Zaklouta and B. Stanciulescu, “Real-time traffic-sign recognition using tree classifiers,” IEEE Trans. Intell. Transp. Syst. 13, 1507–1514 (2012).
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H. Liu, Y. Liu, and F. Sun, “Traffic sign recognition using group sparse coding,” Inf. Sci. 266, 75–89 (2014).
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Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
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Takarli, F.

F. Takarli, A. Aghagolzadeh, and H. Seyedarabi, “Combination of high-level features with low-level features for detection of pedestrian,” Signal Image Video Process. 10, 93–101 (2016).
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Tombari, F.

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
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Trémeau, A.

M. El Ansari, R. Lahmyed, and A. Trémeau, “A hybrid pedestrian detection system based on visible images and lidar data,” in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (2018), Vol. 5, pp. 27–29.

Triggs, B.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2005), Vol. 1, pp. 886–893.

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Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
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C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

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K. Yang, E. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal Image Video Process. 8, 135–144 (2014).
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X. Yang, “Enhancement for road sign images and its performance evaluation,” Optik 124, 1957–1960 (2013).
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Yao, C.

C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

Yuan, H.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

Zaklouta, F.

F. Zaklouta and B. Stanciulescu, “Real-time traffic-sign recognition using tree classifiers,” IEEE Trans. Intell. Transp. Syst. 13, 1507–1514 (2012).
[Crossref]

Zeng, L.

Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG-ALBP feature for pedestrian detection,” Signal Image Video Process. 8, 125–134 (2014).
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J. Jin, K. Fu, and C. Zhang, “Traffic sign recognition with hinge loss trained convolutional neural networks,” IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014).
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Zhao, H.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

Appl. Soft Comput. (1)

A. Ellahyani, M. El Ansari, and I. El Jaafari, “Traffic sign detection and recognition based on random forests,” Appl. Soft Comput. 46, 805–815 (2016).
[Crossref]

IEEE Trans. Intell. Transp. Syst. (4)

F. Zaklouta and B. Stanciulescu, “Real-time traffic-sign recognition using tree classifiers,” IEEE Trans. Intell. Transp. Syst. 13, 1507–1514 (2012).
[Crossref]

J. Jin, K. Fu, and C. Zhang, “Traffic sign recognition with hinge loss trained convolutional neural networks,” IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014).
[Crossref]

M. Meuter, C. Nunn, S. M. Görmer, S. Müller-Schneiders, and A. Kummert, “A decision fusion and reasoning module for a traffic sign recognition system,” IEEE Trans. Intell. Transp. Syst. 12, 1126–1134 (2011).
[Crossref]

K. Lu, Z. Ding, and S. Ge, “Sparse-representation-based graph embedding for traffic sign recognition,” IEEE Trans. Intell. Transp. Syst. 13, 1515–1524 (2012).
[Crossref]

IET Intell. Transp. Syst. (1)

H. Fleyeh and E. Davami, “Eigen-based traffic sign recognition,” IET Intell. Transp. Syst. 5, 190–196 (2011).
[Crossref]

Inf. Sci. (1)

H. Liu, Y. Liu, and F. Sun, “Traffic sign recognition using group sparse coding,” Inf. Sci. 266, 75–89 (2014).
[Crossref]

Int. J. Adv. Comput. Sci. Appl. (1)

A. Ellahyani, M. El Ansari, I. El Jaafari, and S. Charfi, “Traffic sign detection and recognition using features combination and random forests,” Int. J. Adv. Comput. Sci. Appl. 7, 686–693 (2016).

J. Opt. Soc. Am. A (2)

Mach. Learn. (1)

L. Breiman, “Random forests,” Mach. Learn. 45, 5–32 (2001).
[Crossref]

Multimedia Tools Appl. (3)

S. Charfi and M. El Ansari, “Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images,” Multimedia Tools Appl. 77, 4047–4064 (2018).
[Crossref]

A. Ellahyani and M. El Ansari, “Mean shift and log-polar transform for road sign detection,” Multimedia Tools Appl. 76, 24495–24513 (2017).
[Crossref]

A. Gudigar, S. Chokkadi, U. Raghavendra, and U. R. Acharya, “Multiple thresholding and subspace based approach for detection and recognition of traffic sign,” Multimedia Tools Appl. 76, 6973–6991 (2017).
[Crossref]

Neurocomputing (2)

I. El Jaafari, M. El Ansari, L. Koutti, A. Mazoul, and A. Ellahyani, “Fast spatio-temporal stereo matching for advanced driver assistance systems,” Neurocomputing 194, 24–33 (2016).
[Crossref]

Z.-L. Sun, H. Wang, W.-S. Lau, G. Seet, and D. Wang, “Application of BW-ELM model on traffic sign recognition,” Neurocomputing 128, 153–159 (2014).
[Crossref]

Optik (1)

X. Yang, “Enhancement for road sign images and its performance evaluation,” Optik 124, 1957–1960 (2013).
[Crossref]

Pattern Recognition (2)

S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition 48, 1039–1049 (2015).
[Crossref]

T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition 29, 51–59 (1996).
[Crossref]

Signal Image Video Process. (3)

Y. Liu, L. Zeng, and Y. Huang, “An efficient HOG-ALBP feature for pedestrian detection,” Signal Image Video Process. 8, 125–134 (2014).
[Crossref]

F. Takarli, A. Aghagolzadeh, and H. Seyedarabi, “Combination of high-level features with low-level features for detection of pedestrian,” Signal Image Video Process. 10, 93–101 (2016).
[Crossref]

K. Yang, E. Delp, and E. Du, “Categorization-based two-stage pedestrian detection system for naturalistic driving data,” Signal Image Video Process. 8, 135–144 (2014).
[Crossref]

Other (15)

A. Ellahyani and M. El Ansari, “A new designed descriptor for road sign recognition,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

A. Ellahyani and M. El Ansari, “Complementary features for traffic sign detection and recognition,” in IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) (IEEE, 2016), pp. 1–6.

K. H. Lim, K. P. Seng, and L. M. Ang, “Intra color-shape classification for traffic sign recognition,” in International Computer Symposium (ICS) (IEEE, 2010), pp. 642–647.

W. Liu, J. Lv, H. Gao, B. Duan, H. Yuan, and H. Zhao, “An efficient real-time speed limit signs recognition based on rotation invariant feature,” in IEEE Intelligent Vehicles Symposium (IV) (IEEE, 2011), pp. 1000–1005.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2005), Vol. 1, pp. 886–893.

D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 1918–1921.

P. Sermanet and Y. LeCun, “Traffic sign recognition with multi-scale convolutional networks,” in International Joint Conference on Neural Networks (IJCNN) (IEEE, 2011), pp. 2809–2813.

V. N. Vapnik and V. Vapnik, Statistical Learning Theory (Wiley, 1998), Vol. 1.

N. Christiannini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods (2000).

“German traffic sign detection and recognition benchmark datasets,” 2013, http://benchmark.ini.rub.de/ .

E. Shechtman and M. Irani, “Matching local self-similarities across images and videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2007), pp. 1–8.

M. El Ansari, R. Lahmyed, and A. Trémeau, “A hybrid pedestrian detection system based on visible images and lidar data,” in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (2018), Vol. 5, pp. 27–29.

M. El Ansari and S. Charfi, “Computer-aided system for polyp detection in wireless capsule endoscopy images,” in International Conference on Wireless Networks and Mobile Communications (WINCOM) (IEEE, 2017), pp. 1–6.

M. Souaidi, A. A. Abdelouahad, and M. El Ansari, “A fully automated ulcer detection system for wireless capsule endoscopy images,” in International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (IEEE, 2017), pp. 1–6.

C. Yao, F. Wu, H.-J. Chen, X.-L. Hao, and Y. Shen, “Traffic sign recognition using HOG-SVM and grid search,” in 12th International Conference on Signal Processing (ICSP) (IEEE, 2014), pp. 962–965.

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Figures (9)

Fig. 1.
Fig. 1. Difficult cases for traffic sign classification.
Fig. 2.
Fig. 2. Framework of the method proposed.
Fig. 3.
Fig. 3. Examples of traffic signs with similar appearances.
Fig. 4.
Fig. 4. (left) Image in the Cartesian coordinates system and (right) its corresponding one in the log-polar coordinates system.
Fig. 5.
Fig. 5. (first row) Three traffic sign images; (second row) their corresponding images represented in log-polar space; (third row) images obtained after post-processing of the log-polar images shown in the middle row.
Fig. 6.
Fig. 6. (first row) Traffic sign image and its corresponding scaled and rotated images; (second row) the resulting images obtained after mapping the images of the first row into log-polar coordinates system.
Fig. 7.
Fig. 7. Traffic sign images with similar appearances before and after the log-polar mapping and the post-processing steps.
Fig. 8.
Fig. 8. Subsets of traffic signs in the GTSRB dataset. (a) Speed limit. (b) Other prohibitory. (c) Derestriction. (d) Mandatory. (e) Danger. (f) Unique.
Fig. 9.
Fig. 9. Impact of the threshold T on the CCR.

Tables (4)

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Table 1. Comparison between the CCRs When the Log-Polar and the Cartesian Representations Are Used

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Table 2. Performance of the Features Used on the GTSRB Dataset

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Table 3. CCR and the Average Running Time of the Random Forest and SVM Classifiers

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Table 4. Comparison between the Proposed Classification Method and other Published Methods Using the GTSRB Dataset