C. Premebida, G. Monteiro, U. Nunes, and P. Peixoto, A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking, 2007 IEEE Intelligent Transportation Systems Conference, pp.1044-1049, 2007.
DOI : 10.1109/ITSC.2007.4357637

K. Kidono, T. Miyasaka, A. Watanabe, T. Naito, and J. Miura, Pedestrian recognition using high-definition LIDAR, Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp.5-9, 2011.
DOI : 10.7210/jrsj.29.963

C. Premebida and U. J. Nunes, Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection, The International Journal of Robotics Research, vol.1, issue.11, pp.10-1177, 2013.
DOI : 10.1177/0278364912470012

M. Bertozzi, A. Broggi, C. H. Gomez, R. Fedriga, and G. Vezzoni, Pedestrian Detection in Far Infrared Images based on the use of Probabilistic Templates, 2007 IEEE Intelligent Vehicles Symposium, pp.13-15, 2007.
DOI : 10.1109/IVS.2007.4290135

M. Bertozzi, A. Broggi, C. Caraffi, M. Del-rose, M. Felisa et al., Pedestrian detection by means of far-infrared stereo vision. Comput. Vision Image Underst, pp.194-204, 2007.
DOI : 10.1016/j.cviu.2006.07.016

K. Yamaguchi, A. C. Berg, L. E. Ortiz, and T. L. Berg, Who are you with and where are you going?, CVPR 2011, pp.20-25, 2011.
DOI : 10.1109/CVPR.2011.5995468
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.232.8163

P. Dollar, C. Wojek, B. Schiele, and P. Perona, Pedestrian Detection: An Evaluation of the State of the Art, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.4, pp.743-761, 2012.
DOI : 10.1109/TPAMI.2011.155

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.
DOI : 10.1109/TIP.2011.2142006

T. Gandhi and M. M. Trivedi, Pedestrian Protection Systems: Issues, Survey, and Challenges, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.3, pp.413-430, 2007.
DOI : 10.1109/TITS.2007.903444
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.6405

D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, Survey of Pedestrian Detection for Advanced Driver Assistance Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.7, pp.1239-1258, 2010.
DOI : 10.1109/TPAMI.2009.122

M. Enzweiler and D. M. Gavrila, Monocular Pedestrian Detection: Survey and Experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, pp.31-2179, 2009.
DOI : 10.1109/TPAMI.2008.260
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5794

M. Enzweiler, P. Kanter, and D. Gavrila, Monocular pedestrian recognition using motion parallax, 2008 IEEE Intelligent Vehicles Symposium, pp.4-6, 2008.
DOI : 10.1109/IVS.2008.4621169
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8005

S. Nedevschi, S. Bota, and C. Tomiuc, Stereo-Based Pedestrian Detection for Collision-Avoidance Applications, IEEE Transactions on Intelligent Transportation Systems, vol.10, issue.3, pp.380-391, 2009.
DOI : 10.1109/TITS.2008.2012373
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.4656

M. Bajracharya, B. Moghaddam, A. Howard, S. Brennan, and L. H. Matthies, A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle, The International Journal of Robotics Research, vol.30, issue.7, pp.1466-1485, 2009.
DOI : 10.1177/0278364909341884

A. Ess, B. Leibe, and L. Van-gool, Depth and Appearance for Mobile Scene Analysis, 2007 IEEE 11th International Conference on Computer Vision, pp.14-21, 2007.
DOI : 10.1109/ICCV.2007.4409092

D. M. Gavrila and S. Munder, Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle, International Journal of Computer Vision, vol.10, issue.6, pp.41-59, 2007.
DOI : 10.1007/s11263-006-9038-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.8510

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, European Conference on Computer Vision, pp.428-441, 2006.
DOI : 10.1023/A:1008162616689
URL : https://hal.archives-ouvertes.fr/inria-00548587

M. Rohrbach, M. Enzweiler, and D. M. Gavrila, High-Level Fusion of Depth and Intensity for Pedestrian Classification, In Pattern Recognition, vol.75, issue.2, pp.101-110, 2009.
DOI : 10.1007/s11263-006-0027-7

C. Wojek, S. Walk, and B. Schiele, Multi-cue onboard pedestrian detection, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.20-25, 2009.
DOI : 10.1109/CVPR.2009.5206638

S. Walk, N. Majer, K. Schindler, and B. Schiele, New features and insights for pedestrian detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.13-18, 2010.
DOI : 10.1109/CVPR.2010.5540102
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.7144

S. Walk, K. Schindler, and B. Schiele, Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo, European Conference on Computer Vision, pp.182-195, 2010.
DOI : 10.1007/978-3-642-15567-3_14

M. Enzweiler, A. Eigenstetter, B. Schiele, and D. M. Gavrila, Multi-cue pedestrian classification with partial occlusion handling, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.13-18, 2010.
DOI : 10.1109/CVPR.2010.5540111
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.3291

S. J. Krotosky and M. M. Trivedi, A Comparison of Color and Infrared Stereo Approaches to Pedestrian Detection, 2007 IEEE Intelligent Vehicles Symposium, pp.13-15
DOI : 10.1109/IVS.2007.4290095

R. Labayrade, D. Aubert, and J. P. Tarel, Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation, Intelligent Vehicle Symposium, 2002. IEEE, pp.17-21, 2002.
DOI : 10.1109/IVS.2002.1188024

S. J. Krotosky and M. M. Trivedi, On Color-, Infrared-, and Multimodal-Stereo Approaches to Pedestrian Detection, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.4, pp.619-629, 2007.
DOI : 10.1109/TITS.2007.908722
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.2595

M. Bertozzi, E. Binelli, A. Broggi, and M. Rose, Stereo Vision-based approaches for Pedestrian Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Workshops, pp.25-41, 2005.
DOI : 10.1109/CVPR.2005.534
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.7254

M. Bertozzi, A. Broggi, A. Lasagni, and M. Rose, Infrared stereo vision-based pedestrian detection, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.6-8, 2005.
DOI : 10.1109/IVS.2005.1505072

A. Miron, B. Besbes, A. Rogozan, S. Ainouz, and A. Bensrhair, Intensity self similarity features for pedestrian detection in Far-Infrared images, 2012 IEEE Intelligent Vehicles Symposium, pp.3-7, 2012.
DOI : 10.1109/IVS.2012.6232227

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

G. Overett, L. Petersson, N. Brewer, L. Andersson, and N. Pettersson, A new pedestrian dataset for supervised learning, 2008 IEEE Intelligent Vehicles Symposium, pp.4-6, 2008.
DOI : 10.1109/IVS.2008.4621297
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.4512

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.16-21, 2012.
DOI : 10.1109/CVPR.2012.6248074

J. P. Simon-lynen and . Dataset, Available online, 2014.

J. W. Davis and M. A. Keck, A Two-Stage Template Approach to Person Detection in Thermal Imagery, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, pp.5-7, 2005.
DOI : 10.1109/ACVMOT.2005.14

J. W. Davis and V. Sharma, Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vision Image Underst, pp.162-182, 2007.

M. Brown and S. Susstrunk, Multi-spectral SIFT for scene category recognition, CVPR 2011, pp.20-25, 2011.
DOI : 10.1109/CVPR.2011.5995637
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.229.1729

D. Olmeda, C. Premebida, U. Nunes, J. M. Armingol, and A. De-la-escalera, Pedestrian detection in far infrared images, Integr. Comput.-Aided Eng, vol.20, pp.347-360, 2013.

M. Bertozzi, A. Broggi, M. Felisa, G. Vezzoni, and M. Del-rose, Low-level Pedestrian Detection by means of Visible and Far Infra-red Tetra-vision, 2006 IEEE Intelligent Vehicles Symposium, pp.13-15, 2006.
DOI : 10.1109/IVS.2006.1689633

B. Besbes, S. Ammar, Y. Kessentini, A. Rogozan, and A. Bensrhair, Evidential combination of SVM road obstacle classifiers in visible and far infrared images, 2011 IEEE Intelligent Vehicles Symposium (IV), pp.5-9, 2011.
DOI : 10.1109/IVS.2011.5940529

A. Apatean, A. Rogozan, and A. Bensrhair, Visible-infrared fusion schemes for road obstacle classification, Transportation Research Part C: Emerging Technologies, vol.35, pp.180-192, 2013.
DOI : 10.1016/j.trc.2013.07.003

X. Wang, T. X. Han, and S. Yan, An HOG-LBP human detector with partial occlusion handling, 2009 IEEE 12th International Conference on Computer Vision, pp.32-39, 2009.
DOI : 10.1109/ICCV.2009.5459207

B. Jun, I. Choi, and D. Kim, Local Transform Features and Hybridization for Accurate Face and Human Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.6, pp.1423-1436, 2013.
DOI : 10.1109/TPAMI.2012.219

R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, LIBLINEAR: A library for large linear classification, J. Mach. Learn. Res, vol.9, pp.1871-1874, 2008.

A. Geiger, M. Roser, and R. Urtasun, Efficient Large-Scale Stereo Matching, Asian Conference of Computer Vision, pp.25-38, 2011.
DOI : 10.1007/BFb0014497
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.889