I. Segmentation and .. , 62 Color Enhancement, Morphological Filters, vol.64

.. Category-detection, 64 Data set and Evaluation, p.68

T. System and .. , 3 Overall Performance of the, p.78

G. Alefs, H. Eschemann, &. C. Ramoser, and . Beleznai, Road Sign Detection from Edge Orientation Histograms, 2007 IEEE Intelligent Vehicles Symposium, pp.993-998, 2007.
DOI : 10.1109/IVS.2007.4290246

E. L. Allwein, R. E. Schapire, and &. Y. Singer, Reducing multiclass to binary: a unifying approach for margin classifiers, The Journal of Machine Learning Research, vol.1, pp.113-141, 2001.

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, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.255-260, 2005.
DOI : 10.1109/IVS.2005.1505111

A. Bargeton, F. Moutarde, F. Nashashibi, and &. B. Bradai, Improving pan-European speed-limit signs recognition with a new “global number segmentation” before digit recognition, 2008 IEEE Intelligent Vehicles Symposium, 2008.
DOI : 10.1109/IVS.2008.4621168

N. Barnes, A. Zelinsky, and &. L. Fletcher, Real-time speed sign detection using the radial symmetry detector. Intelligent Transportation Systems, IEEE Transactions on, vol.9, issue.2, pp.322-332, 2008.

X. Baró, S. Escalera, J. Vitrià, O. Pujol, and &. P. Radeva, Traffic sign recognition using evolutionary adaboost detection and forest- ECOC classification. Intelligent Transportation Systems, IEEE Transactions on, vol.10, issue.1, pp.113-126, 2009.

S. Maldonado-bascon, S. Lafuente-arroyo, P. Gil-jimenez, H. Gomez-moreno, and &. F. Lopez-ferreras, Road-Sign Detection and Recognition Based on Support Vector Machines, IEEE transactions on intelligent transportation systems, pp.264-278, 2007.
DOI : 10.1109/TITS.2007.895311

S. Maldonado-bascon, J. Acevedo-rodriguez, S. L. Arroyo, A. Fernndez-caballero, and &. F. Lopez-ferreras, An optimization on pictogram identification for the road-sign recognition task using SVMs, Computer Vision and Image Understanding, vol.114, issue.3, pp.373-383, 2010.
DOI : 10.1016/j.cviu.2009.12.002

H. Bay, A. Ess, T. Tuytelaars, and &. L. Van-gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
DOI : 10.1016/j.cviu.2007.09.014

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. S. Beis and &. D. Lowe, Shape indexing using approximate nearestneighbour search in high-dimensional spaces, Computer Vision and Pattern Recognition, pp.1000-1006, 1997.

F. Boi and &. Gagliardini, A Support Vector Machines network for traffic sign recognition, The 2011 International Joint Conference on Neural Networks, 2011.
DOI : 10.1109/IJCNN.2011.6033503

A. Bosch, A. Zisserman, and &. X. Munoz, Scene Classification Via pLSA, Computer Vision?ECCV, pp.517-530, 2006.
DOI : 10.1007/11744085_40

]. A. Bosch-07a, A. Bosch, &. X. Zisserman, and . Munoz, Image Classification using Random Forests and Ferns, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4409066

]. A. Bosch-07b, A. Bosch, &. X. Zisserman, and . Munoz, Representing shape with a spatial pyramid kernel, Proceedings of the 6th ACM international conference on Image and video retrieval, CIVR '07, pp.401-408, 2007.
DOI : 10.1145/1282280.1282340

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

M. Chen, &. T. Chen, and . Gao, Detection and Recognition of Alert Traffic Signs, 2008.

D. Ciresan, U. Meier, J. Masci, and &. Schmidhuber, A committee of neural networks for traffic sign classification, The 2011 International Joint Conference on Neural Networks, 2011.
DOI : 10.1109/IJCNN.2011.6033458

I. M. Creusen, R. G. Wijnhoven, E. Herbschleb, and &. P. De-with, Color exploitation in hog-based traffic sign detection, 2010 IEEE International Conference on Image Processing, pp.2669-2672, 2010.
DOI : 10.1109/ICIP.2010.5651637

C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and &. Seelen, Walking pedestrian recognition. Intelligent Transportation Systems, IEEE Transactions on, vol.1, issue.3, pp.155-163, 2000.
DOI : 10.1109/6979.892152

URL : http://hdl.handle.net/11858/00-001M-0000-0013-E45F-2

A. De-la-escalera, L. E. Moreno, M. A. Salichs, and &. J. , Road traffic sign detection and classification, IEEE Transactions on Industrial Electronics, vol.44, issue.6, pp.848-859, 1997.
DOI : 10.1109/41.649946

A. De-la-escalera, J. M. Armingol, and &. M. Mata, Traffic sign recognition and analysis for intelligent vehicles, Image and Vision Computing, vol.21, issue.3, pp.247-258, 2003.
DOI : 10.1016/S0262-8856(02)00156-7

G. Thomas, &. Dietterich, and . Bakiri, Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, vol.2, pp.263-286, 1995.

P. Dollar, C. Wojek, B. Schiele, and &. P. Perona, Pedestrian detection: A benchmark, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206631

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. Domeniconi, J. Peng, and &. D. Gunopulos, Locally adaptive metric nearest-neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.9, pp.1281-1285, 2002.
DOI : 10.1109/TPAMI.2002.1033219

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. Eichhorn and &. O. Chapelle, Object categorization with SVM: kernels for local features, 2004.

M. Enzweiler and &. Gavrila, Monocular Pedestrian Detection: Survey and Experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, 2009.
DOI : 10.1109/TPAMI.2008.260

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. W. Fang, &. S. Chen, and . Fuh, Road-sign detection and tracking, IEEE Transactions on Vehicular Technology, vol.52, issue.5, pp.1329-1341, 2003.
DOI : 10.1109/TVT.2003.810999

H. Fleyeh and &. M. Dougherty, Traffic sign classification using invariant features and Support Vector Machines, 2008 IEEE Intelligent Vehicles Symposium, pp.530-535, 2008.
DOI : 10.1109/IVS.2008.4621132

U. Franke, D. Gavrila, S. Görzig, F. Lindner, F. Paetzold et al., Autonomous driving approaches downtown, IEEE Intelligent Systems, vol.13, issue.6, pp.1-14, 1999.
DOI : 10.1109/5254.736001

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

Y. Freund, &. Robert, and E. Schapire, Experiments with a New Boosting Algorithm, Proceedings of the Thirteenth International Conference on Machine Learning, pp.148-156, 1996.

Y. Freund and &. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

X. W. Gao, L. Podladchikova, D. Shaposhnikov, K. Hong, and &. N. Shevtsova, Recognition of traffic signs based on their colour and shape features extracted using human vision models, Journal of Visual Communication and Image Representation, vol.17, issue.4, pp.675-685, 2006.
DOI : 10.1016/j.jvcir.2005.10.003

M. Sotelo and &. E. Martin-gorostiza, Fast road sign detection using hough transform for assisted driving of road vehicles, Computer Aided Systems Theory?EUROCAST 2005, pp.543-548, 2005.

D. Gavrila and &. V. Philomin, Real-time object detection for "smart" vehicles, Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999.
DOI : 10.1109/ICCV.1999.791202

P. Gehler and &. S. Nowozin, On feature combination for multiclass object classification, 2009 IEEE 12th International Conference on Computer Vision, pp.221-228, 2009.
DOI : 10.1109/ICCV.2009.5459169

P. Geurts, D. Ernst, and &. L. Wehenkel, Extremely randomized trees, Machine Learning, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1

URL : https://hal.archives-ouvertes.fr/hal-00341932

K. Grauman and P. Perona, The pyramid match kernel: Efficient learning with sets of features, Journal of Machine Learning Research, vol.8, p.2007, 2007.

V. Guruswami and &. A. Sahai, Multiclass learning, boosting, and errorcorrecting codes, pp.145-155, 1999.

I. Guyon and &. A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

T. K. Ho, Random decision forests Towards reliable traffic sign recognition, icdar Intelligent Vehicles Symposium, pp.278-324, 1995.

P. , G. Jimenez, S. Lafuente-arroyo, H. Gomez-moreno, F. Lopez-ferreras et al., Traffic sign shape classification evaluation. Part II. FFT applied to the signature of blobs, Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pp.607-612, 2005.

M. Golawala and &. J. Van-hulse, An Empirical Study of Learning from Imbalanced Data Using Random Forest, 19th IEEE International Conference on Tools with Artificial Intelligence, pp.310-317, 2007.

H. Kim, S. Pang, H. Je, D. Kim, &. Sung et al., Pattern classification using support vector machine ensemble, Proceedings. 16th International Conference on, pp.160-163, 2002.

]. K. Kira and &. L. , A Practical Approach to Feature Selection, Proceedings of the ninth international workshop on Machine learning, pp.249-256, 1992.
DOI : 10.1016/B978-1-55860-247-2.50037-1

R. Kohavi, &. George, and H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

W. Kuo and &. Lin, Two-Stage Road Sign Detection and Recognition, Multimedia and Expo, 2007 IEEE International Conference on, pp.1427-1430, 2007.
DOI : 10.1109/ICME.2007.4284928

S. Lazebnik, C. Schmid, and &. J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

]. T. Le, S. Tran, S. Mita, &. T. Nguyen-lin, and &. G. Wahba, Real Time Traffic Sign Detection Using Color and Shape-Based Features. Intelligent Information and Database Systems Multicategory support vector machines, Journal of the American Statistical Association, vol.99, issue.465, pp.268-278, 2004.

V. Lepetit and &. P. Fua, Keypoint recognition using randomized trees, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.9, pp.1465-1479, 2006.
DOI : 10.1109/TPAMI.2006.188

L. Li, Multiclass boosting with repartitioning, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.569-576, 2006.
DOI : 10.1145/1143844.1143916

Z. Li, Z. Wei, B. Yin, X. Ji, and &. R. Shan, Pedestrian Detection Based on a New Two-Step Framework, 2010 Second International Workshop on Education Technology and Computer Science, pp.56-59, 2010.
DOI : 10.1109/ETCS.2010.160

. H. Lim-jr-10-]-k, K. P. Lim-jr, &. L. Seng-jr, and . Ang-jr, Intra color-shape classification for traffic sign recognition, ICS, pp.642-647, 2010.

&. C. Lin and . Hsu, Boosting multiclass learning with repeating codes, pp.591-598, 2006.
DOI : 10.1093/bioinformatics/btm497

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/23/24/3374

H. Liu and &. L. Yu, Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.4, pp.491-502, 2005.

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

K. Mikolajczyk and &. C. Schmid, Indexing based on scale invariant interest points, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.525-531, 2001.
DOI : 10.1109/ICCV.2001.937561

URL : https://hal.archives-ouvertes.fr/inria-00548276

&. C. Mikolajczyk and . Schmid, An Affine Invariant Interest Point Detector, Computer Vision ECCV, pp.128-142, 2002.
DOI : 10.1007/3-540-47969-4_9

URL : https://hal.archives-ouvertes.fr/inria-00548252

K. Mikolajczyk, C. Schmid, and &. A. Zisserman, Human Detection Based on a Probabilistic Assembly of Robust Part Detectors, Computer Vision-ECCV, pp.69-82, 2004.
DOI : 10.1007/978-3-540-24670-1_6

URL : https://hal.archives-ouvertes.fr/inria-00548537

J. Miura, T. Kanda, and &. Y. Shirai, An active vision system for realtime traffic sign recognition, Proc. IEEE Inteligent transportation systems, pp.52-57, 2000.

F. Moosmann, B. Triggs, and &. F. Jurie, Fast discriminative visual codebooks using randomized clustering forests Advances in neural information processing systems, p.985, 2007.

F. Moutarde, A. Bargeton, A. Herbin, and &. L. Chanussot, Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system, 2007 IEEE Intelligent Vehicles Symposium, pp.1122-1126, 2007.
DOI : 10.1109/IVS.2007.4290268

URL : https://hal.archives-ouvertes.fr/hal-00422588

F. Moutarde, B. Stanciulescu, and &. A. Breheret, Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features . Workshop on Planning, Perception, and Navigation for Intelligent Vehicles, IROS, BIBLIOGRAPHY [Munder 06] S. Munder & DM Gavrila. An Experimental Study on Pedestrian Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp.1863-1868, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00320888

J. Mutch and &. Dg-lowe, Multiclass Object Recognition with Sparse, Localized Features, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.200

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

A. Kouzani, Detection and classification of road signs in natural environments, Neural Computing & Applications, vol.17, issue.10, pp.265-289, 1007.

H. Ohara, I. Nishikawa, S. Miki, and &. N. Yabuki, Detection and recognition of road signs using simple layered neural networks, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02., pp.626-630, 2002.
DOI : 10.1109/ICONIP.2002.1198133

S. Paisitkriangkrai, C. Shen, and &. J. Zhang, An Experimental Evaluation of Local Features for Pedestrian Classification, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), pp.53-60, 2007.
DOI : 10.1109/DICTA.2007.4426775

J. Ponce, M. Berg, . Everingham, M. Da-forsyth, S. Hebert et al., Dataset Issues in Object Recognition, Lecture Notes in Computer Science, vol.4170, p.29, 2006.
DOI : 10.1007/11957959_2

URL : https://hal.archives-ouvertes.fr/inria-00548595

X. Qingsong, S. Juan, and &. L. Tiantian, A detection and recognition method for prohibition traffic signs, 2010 International Conference on Image Analysis and Signal Processing, pp.583-586, 2010.
DOI : 10.1109/IASP.2010.5476048

R. Rajesh, K. Rajeev, K. Suchithra, V. P. Lekhesh, V. Gopakumar et al., Coherence vector of Oriented Gradients for traffic sign recognition using Neural Networks, The 2011 International Joint Conference on Neural Networks, 2011.
DOI : 10.1109/IJCNN.2011.6033318

]. F. Ren, J. Huang, R. Jiang, and &. R. Klette, General traffic sign recognition by feature matching, 2009 24th International Conference Image and Vision Computing New Zealand, pp.409-414, 2009.
DOI : 10.1109/IVCNZ.2009.5378370

P. C. Ribeiro, &. J. Santos, and . Victor, Human activity recognition from video: Modeling, feature selection and classification architecture, Proceedings of International Workshop on Human Activity Recognition and Modelling. Citeseer, 2005.

A. Ruta, Y. Li, M. Uxbridge, F. Porikli, S. Watanabe et al., A New Approach for In-Vehicle Camera Traffic Sign Detection and Recognition, Proc. IAPR Conference on Machine Vision Applications, 2009.

A. Ruta, Y. Li, and &. X. Liu, Real-time traffic sign recognition from video by class-specific discriminative features, Pattern Recognition, vol.43, issue.1, pp.416-430, 2010.
DOI : 10.1016/j.patcog.2009.05.018

A. Ruta, F. Porikli, S. Watanabe, and &. Y. Li, In-vehicle camera traffic sign detection and recognition, Machine Vision and Applications, pp.1-17, 2011.
DOI : 10.1007/s00138-009-0231-x

A. H. Sahoolizadeh, B. Z. Heidari, and &. C. Dehghani, A New Face Recognition Method using PCA, LDA and Neural Network, International Journal of Computer Science and Engineering, vol.2, issue.4, pp.218-223, 2008.

R. E. Schapire, Using output codes to boost multiclass learning problems, MACHINE LEARNING-INTERNATIONAL WORK- SHOP THEN CONFERENCE, pp.313-321, 1997.

R. E. Schapire, A brief introduction to boosting, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence table of contents, pp.1401-1406, 1999.

P. Sermanet and &. Lecun, Traffic sign recognition with multi-scale Convolutional Networks, The 2011 International Joint Conference on Neural Networks, 2011.
DOI : 10.1109/IJCNN.2011.6033589

]. S. Shalev-shwartz-07, Y. Shalev-shwartz, &. N. Singer, and . Srebro, Pegasos, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.807-814, 2007.
DOI : 10.1145/1273496.1273598

J. Shotton, M. Johnson, and &. R. Cipolla, Semantic texton forests for image categorization and segmentation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587503

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. A. Sotelo, I. Parra, D. Fernandez, and &. E. Naranjo, Pedestrian Detection Using SVM and Multi-Feature Combination, 2006 IEEE Intelligent Transportation Systems Conference, pp.103-108, 2006.
DOI : 10.1109/ITSC.2006.1706726

B. Stanciulescu, A. Breheret, and &. F. Moutarde, Introducing New AdaBoost Features for Real-Time Vehicle Detection, Proceedings of COGIS2007 Cognitive Systems with Interactive Sensors, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00422587

F. Suard, A. Rakotomamonjy, and &. A. Bensrhair, Object Categorization Using Kernels Combining Graphs and Histograms of Gradients, Lecture Notes in Computer Science, vol.4142, p.23, 2006.
DOI : 10.1007/11867661_3

R. Tibshirani and &. T. Hastie, Margin trees for high-dimensional classification, The Journal of Machine Learning Research, vol.8, pp.637-652, 2007.

]. Z. Tu, Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering, 2005.

T. Tuytelaars and &. K. Mikolajczyk, Survey on local invariant features. FnT Computer Graphics and Vision, pp.1-94, 2008.

P. Viola and &. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.511-518, 2001.
DOI : 10.1109/CVPR.2001.990517

M. J. Jones and &. D. Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision, vol.63, issue.2, pp.153-161, 2005.

T. Watanabe, S. Ito, and &. K. Yokoi, Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection, Advances in Image and Video Technology, pp.37-47, 2009.
DOI : 10.1007/11744047_33

]. J. Weston and &. C. Watkins, Support vector machines for multi-class pattern recognition, Proceedings of the seventh European symposium on artificial neural networks, pp.219-224, 1999.

]. J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio et al., Feature selection for SVMs Advances in neural information processing systems, pp.668-674, 2001.

C. Wohler and &. J. Anlauf, An adaptable time-delay neural-network algorithm for image sequence analysis, IEEE Transactions on Neural Networks, vol.10, issue.6, pp.1531-1536, 1999.
DOI : 10.1109/72.809100

D. Wu, K. P. Bennett, N. Cristianini, and &. J. Shawe-taylor, Enlarging the Margins in Perceptron Decision Trees, Machine Learning, pp.295-313, 2000.

D. Wu, &. K. Zhang, and . Wang, Fisherpalms based palmprint recognition, Pattern Recognition Letters, vol.24, issue.15, pp.2829-2838, 2003.
DOI : 10.1016/S0167-8655(03)00141-7

L. F. Xie, C. H. Liu, &. Y. Li, and . Qu, Unifying visual saliency with HOG feature learning for traffic sign detection, Intelligent Vehicles Symposium, pp.24-29, 2009.