A. Angelova, A. Krizhevsky, and V. Vanhoucke, Pedestrian detection with a large-field-of-view deep network, IEEE International Conference on Robotics and Automation, ICRA 2015, pp.704-711, 2015.

R. Benenson, M. Omran, J. Hosang, and B. Schiele, Ten years of pedestrian detection, what have we learned?, Computer Vision -ECCV 2014 Workshops, pp.613-627, 2015.

A. Borichev and Y. Tomilov, Optimal polynomial decay of functions and operator semigroups, Mathematische Annalen, vol.347, issue.2, pp.455-478, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01257778

L. Bottou, Stochastic gradient descent tricks, Neural Networks: Tricks of the Trade: Second Edition, pp.421-436, 2012.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05, vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

P. Dollar, Z. Tu, P. Perona, and S. Belongie, Integral channel features, Proc. BMVC, vol.11, pp.91-92, 2009.

P. Dollar, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: An evaluation of the state of the art, IEEE Trans. Pattern Anal. Mach. Intell, vol.34, issue.4, pp.743-761, 2012.

J. Duchi, E. Hazan, and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, 2010.

M. Eisenbach, D. Seichter, T. Wengefeld, and H. M. Gross, Cooperative multi-scale convolutional neural networks for person detection, 2016 International Joint Conference on Neural Networks (IJCNN), pp.267-276, 2016.

M. Enzweiler, A. Eigenstetter, B. Schiele, and D. M. Gavrila, Multicue pedestrian classification with partial occlusion handling, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.990-997, 2010.

M. Enzweiler and D. M. Gavrila, A multilevel mixture-of-experts framework for pedestrian classification, IEEE Transactions on Image Processing, vol.20, issue.10, pp.2967-2979, 2011.

P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell, vol.32, issue.9, pp.1627-1645, 2010.

H. Fukui, T. Yamashita, Y. Yamauchi, H. Fujiyoshi, and H. Murase, Pedestrian detection based on deep convolutional neural network with ensemble inference network, 2015 IEEE Intelligent Vehicles Symposium (IV), pp.223-228, 2015.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS10). Society for Artificial Intelligence and Statistics, 2010.

J. Hosang, M. Omran, R. Benenson, and B. Schiele, Taking a deeper look at pedestrians, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Convolutional architecture for fast feature embedding, 2014.

C. Karaoguz and A. Gepperth, Incremental learning for bootstrapping object classifier models, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.1242-1248, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01418160

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.

R. Matei and P. Ungureanu, A class of gaussian-shaped cnn filter banks, 11th International Workshop on Cellular Neural Networks and Their Applications, pp.135-139, 2008.

W. Ouyang and X. Wang, A discriminative deep model for pedestrian detection with occlusion handling, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3258-3265, 2012.

W. Ouyang, H. Zhou, H. Li, Q. Li, J. Yan et al., Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.99, pp.1-1, 2017.

S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.

Q. Sinno-jialin-pan and . Yang, A survey on transfer learning, IEEE Trans. on Knowl. and Data Eng, vol.22, issue.10, pp.1345-1359, 2010.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. D?-anut¸ovidiuanut¸anut¸ovidiu-pop, F. Rogozan, A. Nashashibi, and . Bensrhair, Fusion of stereo vision for pedestrian recognition using convolutional neural networks, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp.47-52, 2017.

A. D?-anut¸ovidiuanut¸anut¸ovidiu-pop, F. Rogozan, A. Nashashibi, and . Bensrhair, Incremental cross-modality deep learning for pedestrian recognition, 28th IEEE Intelligent Vehicles Symposium (IV), pp.523-528, 2017.

A. D?-anut¸ovidiuanut¸anut¸ovidiu-pop, F. Rogozan, A. Nashashibi, and . Bensrhair, Pedestrian recognition through different cross-modality deep learning methods, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp.133-138, 2017.

W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, Human detection using partial least squares analysis, IEEE 12th International Conference on Computer Vision, pp.24-31, 2009.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res, vol.15, issue.1, pp.1929-1958, 2014.

P. Sun, Exponential decay of expansive constants, Science China Mathematics, vol.56, issue.10, pp.2063-2067, 2013.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed et al., Going deeper with convolutions, 2014.

T. Tieleman and G. Hinton, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Networks for Machine Learning, 2012.

D. Vazquez, A. M. Lopez, J. Marin, D. Ponsa, and D. Geronimo, Virtual and real world adaptation for pedestrian detection, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, issue.4, pp.797-809, 2014.

A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple kernels for object detection, Proceedings of the International Conference on Computer Vision (ICCV), 2009.

J. Wagner, V. Fischer, M. Herman, and S. Behnke, Multispectral pedestrian detection using deep fusion convolutional neural networks, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp.509-514, 2016.

A. C. Zhou-wang, H. R. Bovik, E. P. Sheikh, and . Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.

P. Xiaogang, W. Wei, Q. Ke, J. Ye, and . Jiao, Pedestrian detection with deep convolutional neural network, Computer Vision -ACCV 2014 Workshops, pp.354-365, 2014.

, His research interests include classification, detection, actions prediction and tracking of road users based on vision, radar, and sensors fusion methods for the intelligent vehicle, 2014.

, Heterogeneous Data Fusion for Audio-Visual Speech Recognition". Her current research activity is concerned with deep learning, multiple kernels, hybrid models and fusion schemes and the corresponding adaptation methods, for automatic classification and understanding. Her privileged application areas are image and text mining, 2000.

, Abdelaziz Bensrhair graduated with the Master of Science in electrical engineering (1989) and the Ph

, He is currently a Professor in Information Systems Architecture Department, head of Intelligent Transportation Systems Division (2007-2012) and co-director of the Computer Science, Information Processing, Computer science (1992) at the University of, pp.2002-2016

, His main research topics are in environment perception and multi-sensor fusion, vehicle positioning and environment 3D modeling with main applications in Intelligent Transport Systems and Robotics. He is the author of numerous publications and patents in the field of ITS and ADAS systems, Fawzi Nashashibi 51 years, is a senior researcher and has been the Program Manager of RITS Team at INRIA (Paris-Rocquencourt) since 2010. Fawzi Nashashibi has a Masters Degree in Automation