M. Awais and K. Mikolajczyk, Feature Pairs Connected by Lines for Object Recognition, 2010 20th International Conference on Pattern Recognition, pp.3093-3096, 2010.
DOI : 10.1109/ICPR.2010.757

B. Leibe, A. Leonardis, and B. Schiele, Robust Object Detection with Interleaved Categorization and Segmentation, International Journal of Computer Vision, vol.73, issue.2, pp.259-289, 2008.
DOI : 10.1007/s11263-007-0095-3

E. Seemann, M. Fritz, and B. Schiele, Towards Robust Pedestrian Detection in Crowded Image Sequences, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383300

P. F. Felzenszwalb, R. B. Girshick, D. A. Mcallester, and D. Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010.
DOI : 10.1109/TPAMI.2009.167

Z. Sun, G. Bebis, and R. Miller, On-road vehicle detection: A review, IEEE Trans. Pattern Anal. Mach. Intell, vol.28, issue.5, pp.694-711, 2006.

D. Gerónimo, A. M. López, 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

P. Dollár, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: A benchmark, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.304-311, 2009.
DOI : 10.1109/CVPR.2009.5206631

M. Enzweiler and D. Gavrila, Monocular pedestrian detection: Survey and experiments Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.12, pp.2179-2195, 2009.
DOI : 10.1109/tpami.2008.260

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5794

M. Rohrbach, M. Enzweiler, and D. M. Gavrila, High-Level Fusion of Depth and Intensity for Pedestrian Classification, DAGM- Symposium, ser. Lecture Notes in Computer Science, pp.101-110, 2009.
DOI : 10.1007/s11263-006-0027-7

D. 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

J. Gall and V. S. Lempitsky, Class-specific hough forests for object detection, CVPR, pp.1022-1029, 2009.

A. Opelt, A. Pinz, and A. Zisserman, Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection, International Journal of Computer Vision, vol.73, issue.2, pp.16-44, 2008.
DOI : 10.1007/s11263-008-0139-3

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

A. Broggi, C. Caraffi, P. Porta, and P. Zani, The Single Frame Stereo Vision System for Reliable Obstacle Detection Used during the 2005 DARPA Grand Challenge on TerraMax, 2006 IEEE Intelligent Transportation Systems Conference, 2006.
DOI : 10.1109/ITSC.2006.1706831

G. Toulminet, M. Bertozzi, S. Mousset, A. Bensrhair, and A. Broggi, Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis, IEEE Transactions on Image Processing, vol.15, issue.8, 2006.
DOI : 10.1109/TIP.2006.875174

URL : https://hal.archives-ouvertes.fr/hal-00478467

T. Veit, Connexity based fronto-parallel plane detection for stereovision obstacle segmentation, IEEE Int. Conf. on Robotics and Automation, workshop on Safe navigation in open and dynamic environments, 2009.

B. Leibe, K. Schindler, N. Cornelis, and L. J. , Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.10, pp.1683-1698, 2008.
DOI : 10.1109/TPAMI.2008.170

I. P. Alonso, D. F. Llorca, M. ´. Sotelo, L. M. Bergasa, P. R. De-toro et al., Combination of Feature Extraction Methods for SVM Pedestrian Detection, IEEE Transactions on Intelligent Transportation Systems, vol.8, issue.2, pp.292-307, 2007.
DOI : 10.1109/TITS.2007.894194

D. Geronimi, A. D. Sappa, A. Lopez, and D. Ponsa, Adaptive image sampling and windows classification for on-board pedestrian detection, Proceedings of the 5th International Conference on Computer Vision Systems, 2007.

W. R. Mahlisch, R. Schweiger, and K. Dietmayer, Sensorfusion Using Spatio-Temporal Aligned Video and Lidar for Improved Vehicle Detection, 2006 IEEE Intelligent Vehicles Symposium
DOI : 10.1109/IVS.2006.1689665

R. T. Spinello and R. Siegwart, A trained system for multimodal perception in urban environments, Proceedings of the Workshop on Safe Navigation in Open and Dynamic Environment: Applications to Autonomous Vehicles (ICRA)

P. P. Oliveira, U. Nunes, and F. Moita, Semantic fusion of laser and vision in pedestrian detection, Pattern Recognition, vol.43, issue.10, pp.3648-3659, 2010.
DOI : 10.1016/j.patcog.2010.05.014

A. Makris and M. Perrollaz, Urban traffic stereo images dataset, 2011.

M. Perrollaz, A. Spalanzani, and D. Aubert, A probabilistic representation of the uncertainty of stereo-vision and its application to obstacle detection, IEEE Intelligent Vehicles Symp, 2010.

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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

H. Bay, T. Tuytelaars, and L. J. , Surf: Speeded up robust features, ECCV (1), ser. Lecture Notes in Computer Science, pp.404-417, 2006.
DOI : 10.1007/11744023_32

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.679.3046

J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert et al., Dataset Issues in Object Recognition, Toward Category-Level Object Recognition, pp.29-48, 2006.
DOI : 10.1007/11957959_2

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