A. Alahi, R. Ortiz, and P. Vandergheynst, FREAK: Fast Retina Keypoint, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.510-517, 2012.
DOI : 10.1109/CVPR.2012.6247715

Y. He, H. Wang, and B. Zhang, Color-based road detection in urban traffic scenes. Intelligent Transportation Systems, IEEE Transactions on, vol.5, issue.4, pp.309-318, 2004.

H. Kong, J. Audibert, and J. Ponce, General Road Detection From a Single Image, IEEE Transactions on Image Processing, vol.19, issue.8, pp.2211-2220, 2010.
DOI : 10.1109/TIP.2010.2045715

URL : https://hal.archives-ouvertes.fr/hal-00654397

B. Wang and V. Frémont, Fast road detection from color images, 2013 IEEE Intelligent Vehicles Symposium (IV), pp.1209-1214, 2013.
DOI : 10.1109/IVS.2013.6629631

URL : https://hal.archives-ouvertes.fr/hal-00858217

J. Miura, T. Kanda, and Y. Shirai, An active vision system for real-time traffic sign recognition, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493), pp.52-57, 2000.
DOI : 10.1109/ITSC.2000.881017

A. De, L. Escalera, 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.

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

F. Pedro, . Felzenszwalb, B. Ross, D. Girshick, D. Mcallester et al., Object detection with discriminatively trained part-based mod- REFERENCES els. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.9, pp.1627-1645, 2010.

Z. Sun, G. Bebis, and R. Miller, On-road vehicle detection: A review. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.28, issue.5, pp.694-711, 2006.

F. Liu and M. Gleicher, Learning color and locality cues for moving object detection and segmentation, Computer Vision and Pattern Recognition CVPR 2009. IEEE Conference on, pp.320-327, 2009.

H. Jung, J. Ju, and J. Kim, Rigid Motion Segmentation Using Randomized Voting, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.158

C. Yuan, G. Medioni, J. Kang, and I. Cohen, Detecting motion regions in the presence of a strong parallax from a moving camera by multiview geometric constraints. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, issue.9, pp.1627-1641, 2007.

S. Dey, V. Reilly, I. Saleemi, and M. Shah, Detection of Independently Moving Objects in Non-planar Scenes via Multi-Frame Monocular Epipolar Constraint, Computer Vision?ECCV 2012, pp.860-873, 2012.
DOI : 10.1007/978-3-642-33715-4_62

A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke et al., Stereoscopic Scene Flow Computation for 3D Motion Understanding, International Journal of Computer Vision, vol.27, issue.3, pp.29-51, 2011.
DOI : 10.1007/s11263-010-0404-0

V. Romero-cano, I. Juan, and . Nieto, Stereo-based motion detection and tracking from a moving platform, 2013 IEEE Intelligent Vehicles Symposium (IV), pp.499-504, 2013.
DOI : 10.1109/IVS.2013.6629517

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

P. Dollar, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: An evaluation of the state of the art. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.4, pp.743-761, 2012.

J. Marin, D. Vázquez, M. Antonio, J. López, B. Amores et al., Random Forests of Local Experts for Pedestrian Detection, 2013 IEEE International Conference on Computer Vision, pp.2592-2599, 2013.
DOI : 10.1109/ICCV.2013.322

T. Bailey and H. Durrant-whyte, Simultaneous localization and mapping (SLAM): part II, IEEE Robotics & Automation Magazine, vol.13, issue.3, pp.108-117, 2006.
DOI : 10.1109/MRA.2006.1678144

N. Snavely, M. Steven, R. Seitz, and . Szeliski, Skeletal graphs for efficient structure from motion, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587678

T. Miyasaka, Y. Ohama, and Y. Ninomiya, Ego-motion estimation and moving object tracking using multi-layer LIDAR, 2009 IEEE Intelligent Vehicles Symposium, pp.151-156, 2009.
DOI : 10.1109/IVS.2009.5164269

J. Moras, V. Cherfaoui, and P. Bonnifait, Moving Objects Detection by Conflict Analysis in Evidential Grids, 2011 IEEE Intelligent Vehicles Symposium (IV), pp.1122-1127, 2011.
DOI : 10.1109/IVS.2011.5940561

URL : https://hal.archives-ouvertes.fr/hal-00615304

L. Wei, C. Cappelle, Y. Ruichek, and F. Zann, Intelligent Vehicle Localization in Urban Environments Using EKF-based Visual Odometry and GPS Fusion, 18th IFAC World, pp.13776-13781, 2011.
DOI : 10.3182/20110828-6-IT-1002.01965

L. Wei, C. Cappelle, and Y. Ruichek, Camera/laser/gps fusion method for vehicle positioning under extended nis-based sensor validation. Instrumentation and Measurement, IEEE Transactions on, issue.11, pp.623110-3122, 2013.

S. Dey, V. Reilly, I. Saleemi, and M. Shah, Detection of Independently Moving Objects in Non-planar Scenes via Multi-Frame Monocular Epipolar Constraint, ECCV, pp.860-873, 2012.
DOI : 10.1007/978-3-642-33715-4_62

Y. Li and Y. Ruichek, Moving objects detection and recognition using sparse spatial information in urban environments, 2012 IEEE Intelligent Vehicles Symposium, pp.1060-1065, 2012.
DOI : 10.1109/IVS.2012.6232205

Y. Shireen, . Elhabian, M. Khaled, . El-sayed, H. Sumaya et al., Moving object detection in spatial domain using background removal techniques-stateof-art . Recent patents on computer science, pp.32-54, 2008.

Z. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.28-31, 2004.
DOI : 10.1109/ICPR.2004.1333992

A. Zisserman, Multiple view geometry in computer vision, 2003.

A. Kundu, K. Krishna, and J. Sivaswamy, Moving object detection by multi-view geometric techniques from a single camera mounted robot, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4306-4312, 2009.
DOI : 10.1109/IROS.2009.5354227

A. Kundu, C. Krishna, and . Jawahar, Realtime multibody visual SLAM with a smoothly moving monocular camera, 2011 International Conference on Computer Vision, pp.2080-2087, 2011.
DOI : 10.1109/ICCV.2011.6126482

M. Irani and P. Anandan, A unified approach to moving object detection in 2d and 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.20, issue.6, pp.577-589, 1998.

Q. Yu and G. Medioni, A gpu-based implementation of motion detection from a moving platform, Computer Vision and Pattern Recognition Workshops CVPRW'08. IEEE Computer Society Conference on, pp.1-6, 2008.

A. Bugeau and P. Perez, Detection and segmentation of moving objects in complex scenes, Computer Vision and Image Understanding, vol.113, issue.4, pp.459-476, 2009.
DOI : 10.1016/j.cviu.2008.11.005

URL : https://hal.archives-ouvertes.fr/hal-00522620

L. Wang and R. Yang, Global stereo matching leveraged by sparse ground control points, CVPR 2011, pp.3033-3040, 2011.
DOI : 10.1109/CVPR.2011.5995480

H. Hirschmuller, Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.807-814, 2005.
DOI : 10.1109/CVPR.2005.56

S. Moqqaddem, Y. Ruichek, A. Touahni, and . Sbihi, A spectral clustering and kalman filtering based objects detection and tracking REFERENCES 123

S. Moqqaddem, Y. Ruichek, A. Touahni, and . Sbihi, Objects Detection and Tracking Using Points Cloud Reconstructed from Linear Stereo Vision, CURRENT ADVANCEMENTS IN STEREO VISION, p.161, 2012.
DOI : 10.5772/46026

B. Kitt, B. Ranft, and H. Lategahn, Detection and tracking of independently moving objects in urban environments, 13th International IEEE Conference on Intelligent Transportation Systems, pp.1396-1401, 2010.
DOI : 10.1109/ITSC.2010.5625265

P. Lenz, J. Ziegler, A. Geiger, and M. Roser, Sparse scene flow segmentation for moving object detection in urban environments, 2011 IEEE Intelligent Vehicles Symposium (IV), pp.926-932, 2011.
DOI : 10.1109/IVS.2011.5940558

A. Talukder and L. Matthies, Real-time detection of moving objects from moving vehicles using dense stereo and optical flow, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), pp.3718-3725, 2004.
DOI : 10.1109/IROS.2004.1389993

A. Wedel, Stereo scene flow for 3D motion analysis, 2011.
DOI : 10.1007/978-0-85729-965-9

C. Liu, Beyond pixel, Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, ICIMCS '15, 2009.
DOI : 10.1145/2808492.2808573

M. Tao, J. Bai, P. Kohli, and S. Paris, SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm, Computer Graphics Forum, vol.28, issue.4, pp.345-353, 2012.
DOI : 10.1111/j.1467-8659.2012.03013.x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.9792

A. Geiger, M. Roser, and R. Urtasun, Efficient Large-Scale Stereo Matching, Computer Vision?ACCV 2010, pp.25-38, 2011.
DOI : 10.1007/BFb0014497

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.889

G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, 2008.

S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

R. M. Haralick, Propagating covariance in computer vision, Performance Characterization in Computer Vision, pp.95-114, 2000.

C. John and . Clarke, Modelling uncertainty: A primer, p.216198, 1998.

X. Hu and P. Mordohai, A quantitative evaluation of confidence measures for stereo vision. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.11, pp.2121-2133, 2012.

P. C. Mahalanobis, On the generalised distance in statistics, Proceedings of the National Institute of Science of India, pp.49-55, 1936.

Y. Y. Boykov and M. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in ND images, Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, pp.105-112, 2001.

C. Rother, V. Kolmogorov, and A. Blake, "GrabCut", ACM Transactions on Graphics, vol.23, issue.3, pp.309-314, 2004.
DOI : 10.1145/1015706.1015720

A. Herñ-a¡ndez-vela, N. Zlateva, A. Marinov, M. Reyes, P. Radeva et al., Graph cuts optimization for multi-limb human segmentation in depth maps, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.726-732, 2012.

X. Dai, Automatic segmentation fusing color and depth, Pattern Recognition (ICPR), 21st International Conference on, pp.763-766, 2012.

D. Greig, . Porteous, H. Allan, and . Seheult, Exact maximum a posteriori estimation for binary images, Journal of the Royal Statistical Society. Series B (Methodological), pp.271-279, 1989.

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, pp.646-651, 2002.
DOI : 10.1109/IVS.2002.1188024

Z. Hu and K. Uchimura, UV-disparity: an efficient algorithm for stereovision based scene analysis, Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pp.48-54, 2005.

D. Hoiem, A. A. Efros, and M. Hebert, Putting Objects in Perspective, International Journal of Computer Vision, vol.57, issue.2, pp.3-15, 2008.
DOI : 10.1007/s11263-008-0137-5

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.3354-3361, 2012.
DOI : 10.1109/CVPR.2012.6248074

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, issue.11, p.2013
DOI : 10.1177/0278364913491297

C. Liu, Beyond pixel, Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, ICIMCS '15, 2009.
DOI : 10.1145/2808492.2808573

D. Bruce, T. Lucas, and . Kanade, An iterative image registration technique with an application to stereo vision, IJCAI, pp.674-679, 1981.

M. Everingham, L. Van-gool, K. Christopher, J. Williams, A. Winn et al., The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, pp.303-338, 2010.
DOI : 10.1007/s11263-009-0275-4

R. Kumar-namdev, A. Kundu, C. Krishna, and . Jawahar, Motion segmentation of multiple objects from a freely moving monocular camera, Robotics and Automation (ICRA), 2012 IEEE International Conference on, pp.4092-4099, 2012.

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

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, Computer Vision?ECCV 2006, pp.428-441, 2006.
DOI : 10.1023/A:1008162616689

URL : https://hal.archives-ouvertes.fr/inria-00548587

P. Sabzmeydani and G. Mori, Detecting Pedestrians by Learning Shapelet Features, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383134

S. Paisitkriangkrai, C. Shen, and J. Zhang, Fast pedestrian detection using a cascade of boosted covariance features. Circuits and Systems for Video Technology, IEEE Transactions on, vol.18, pp.1140-1151, 2008.

R. Benenson, M. Omran, J. Hosang, and B. Schiele, Ten Years of Pedestrian Detection, What Have We Learned?, ECCV, CVRSUAD workshop, 2014.
DOI : 10.1007/978-3-319-16181-5_47

C. Papageorgiou, T. Evgeniou, and T. Poggio, A trainable pedestrian detection system, Proceeding of Intelligent Vehicles, pp.241-246, 1998.

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

D. Geronimo, A. D. Sappa, D. Ponsa, and A. M. Lopez, 2D???3D-based on-board pedestrian detection system, Computer Vision and Image Understanding, vol.114, issue.5, pp.583-595, 2010.
DOI : 10.1016/j.cviu.2009.07.008

A. Martin, H. Laanaya, and A. Arnold-bos, Evaluation for uncertain image classification and segmentation, Pattern Recognition, vol.39, issue.11, pp.1987-1995, 2006.
DOI : 10.1016/j.patcog.2006.05.015

URL : https://hal.archives-ouvertes.fr/hal-00286593

A. Martin and C. Osswald, Human expert fusion for image classification Information Security: An International Journal, Special issue on fusing uncertain, imprecise and paradoxist information (DSmT), pp.122-143, 2006.

G. David and . Lowe, Distinctive image features from scale-invariant keypoints, International journal of computer vision, vol.60, issue.2, pp.91-110, 2004.

M. Brown, G. David, and . Lowe, Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision, vol.50, issue.1, pp.59-73, 2007.
DOI : 10.1007/s11263-006-0002-3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.193.2527

G. David and . Lowe, Object recognition from local scale-invariant features The proceedings of the seventh IEEE, Computer vision, pp.1150-1157, 1999.

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions, vol.24, issue.4, pp.509-522, 2002.

X. Wang, X. Tony, S. Han, and . 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

Y. Ding and J. Xiao, Contextual boost for pedestrian detection, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.2895-2902, 2012.

D. Hoiem, A. A. Efros, and M. Hebert, Putting objects in perspective, Computer Vision and Pattern Recognition, pp.2137-2144, 2006.

D. Ramanan, Using segmentation to verify object hypotheses In Computer Vision and Pattern Recognition, CVPR'07. IEEE Conference on, pp.1-8, 2007.

Y. Freund, An adaptive version of the boost by majority algorithm, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.293-318, 2001.
DOI : 10.1145/307400.307419

P. Viola and M. J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb

A. Mohan, C. Papageorgiou, and T. Poggio, Example-based object detection in images by components. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.23, issue.4, pp.349-361, 2001.

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

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

B. Wu and R. Nevatia, Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors, International Journal of Computer Vision, vol.I, issue.4, pp.247-266, 2007.
DOI : 10.1007/s11263-006-0027-7

L. Bourdev and J. Malik, Poselets: Body part detectors trained using 3D human pose annotations, 2009 IEEE 12th International Conference on Computer Vision, pp.1365-1372, 2009.
DOI : 10.1109/ICCV.2009.5459303

P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, Object detection with discriminatively trained part-based models . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.9, pp.1627-1645, 2010.

J. Laurens, . Van-der-maaten, O. Eric, H. Postma, . Jaap-van-den et al., Dimensionality reduction: A comparative review, Journal of Machine Learning Research, vol.10, pp.1-4166, 2009.

I. Jolliffe, Principal component analysis, 2005.
DOI : 10.1007/978-1-4757-1904-8

Y. Ke and R. Sukthankar, Pca-sift: A more distinctive representation for local image descriptors, Computer Vision and Pattern Recognition Proceedings of the 2004 IEEE Computer Society Conference on, p.506, 2004.

T. Kobayashi, A. Hidaka, and T. Kurita, Selection of Histograms of Oriented Gradients Features for Pedestrian Detection, Neural Information Processing, pp.598-607, 2008.
DOI : 10.1007/978-3-540-69162-4_62

C. Zeng and H. Ma, Robust head-shoulder detection by pcabased multilevel hog-lbp detector for people counting, Pattern Recognition (ICPR), 2010 International Conference on, pp.2069-2072, 2010.

Y. Freund, R. Schapire, and N. Abe, A short introduction to boosting, Journal-Japanese Society For Artificial Intelligence, vol.14, pp.771-7801612, 1999.

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidencerated predictions, Machine Learning, vol.37, issue.3, pp.297-336, 1999.
DOI : 10.1023/A:1007614523901

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), The Annals of Statistics, vol.28, issue.2, pp.337-407, 2000.
DOI : 10.1214/aos/1016218223

Z. Stan, Z. Li, and . Zhang, Floatboost learning and statistical face detection . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, issue.9, pp.1112-1123, 2004.

A. Ferreira, Survey on boosting algorithms for supervised and semi-supervised learning, 2007.

P. Viola and M. 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.5-11, 2001.
DOI : 10.1109/CVPR.2001.990517

Q. Zhu, M. Yeh, K. Cheng, and S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, Computer Vision and Pattern Recognition, pp.1491-1498, 2006.

P. Viola, J. Michael, D. Jones, and . Snow, Detecting Pedestrians Using Patterns of Motion and Appearance, International Journal of Computer Vision, vol.20, issue.3, pp.153-161, 2005.
DOI : 10.1007/s11263-005-6644-8

Q. Ye, J. Jiao, and B. Zhang, Fast pedestrian detection with multi-scale orientation features and two-stage classifiers, 2010 IEEE International Conference on Image Processing, pp.881-884, 2010.
DOI : 10.1109/ICIP.2010.5654080

V. Vapnik, The nature of statistical learning theory, 2000.

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.990-997, 2010.
DOI : 10.1109/CVPR.2010.5540111

M. Enzweiler, M. Dariu, and . Gavrila, Integrated pedestrian classification and orientation estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.982-989, 2010.
DOI : 10.1109/CVPR.2010.5540110

M. Enzweiler, M. Dariu, and . 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

E. Bernhard, . Boser, M. Isabelle, . Guyon, N. Vladimir et al., A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, pp.144-152, 1992.

M. Szarvas, A. Yoshizawa, M. Yamamoto, and J. Ogata, Pedestrian detection with convolutional neural networks, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.224-229, 2005.
DOI : 10.1109/IVS.2005.1505106

S. Munder and D. M. Gavrila, An experimental study on pedestrian classification . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.28, pp.1863-1868, 2006.

I. P. Alonso, D. F. Llorca, M. A. 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

S. Maji, C. Alexander, J. Berg, and . Malik, Classification using intersection kernel support vector machines is efficient, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587630

P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. Lecun, Pedestrian Detection with Unsupervised Multi-stage Feature Learning, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3626-3633, 2013.
DOI : 10.1109/CVPR.2013.465

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

W. Ouyang, X. Zeng, and X. Wang, Modeling Mutual Visibility Relationship in Pedestrian Detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3222-3229, 2013.
DOI : 10.1109/CVPR.2013.414

W. Ouyang and X. Wang, Joint Deep Learning for Pedestrian Detection, 2013 IEEE International Conference on Computer Vision, pp.2056-2063, 2013.
DOI : 10.1109/ICCV.2013.257

P. Luo, Y. Tian, X. Wang, and X. Tang, Switchable Deep Network for Pedestrian Detection, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.899-906, 2014.
DOI : 10.1109/CVPR.2014.120

O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised learning, 2006.
DOI : 10.7551/mitpress/9780262033589.001.0001

X. Zhu, Semi-supervised learning literature survey, 2006.

X. Zhu, B. Andrew, R. Goldberg, T. Brachman, and . Dietterich, Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.3, issue.1, 2009.
DOI : 10.2200/S00196ED1V01Y200906AIM006

T. Joachims, Transductive inference for text classification using support vector machines, ICML, pp.200-209, 1999.

S. Ben-david, T. Lu, and D. Pál, Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning, COLT, pp.33-44, 2008.

L. Wasserman, D. John, and . Lafferty, Statistical analysis of semi-supervised regression, Advances in Neural Information Processing Systems, pp.801-808, 2007.

R. E. Schapire, M. Rochery, M. Rahim, and N. Gupta, Boosting with prior knowledge for call classification. Speech and Audio Processing, IEEE Transactions on, vol.13, pp.174-181, 2005.

A. Saffari, H. Grabner, and H. Bischof, SERBoost: Semi-supervised Boosting with Expectation Regularization, Computer Vision-ECCV 2008, pp.588-601, 2008.
DOI : 10.1007/978-3-540-88690-7_44

R. Pavan-kumar-mallapragada, A. K. Jin, Y. Jain, and . Liu, Semiboost: Boosting for semi-supervised learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.11, pp.312000-2014, 2009.

K. Chen and S. Wang, Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.33, issue.1, pp.129-143, 2011.

K. Chen and S. Wang, Regularized boost for semi-supervised learning, Advances in neural information processing systems, pp.281-288, 2008.

L. Mason, J. Baxter, P. L. Bartlett, and M. Frean, Functional gradient techniques for combining hypotheses, ADVANCES IN NEU- RAL INFORMATION PROCESSING SYSTEMS, pp.221-246, 1999.

C. Leistner, H. Grabner, and H. Bischof, Semi-supervised boosting using visual similarity learning, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587629

L. Zheng, S. Wang, Y. Liu, and C. Lee, Information theoretic regularization for semi-supervised boosting, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.1017-1026, 2009.
DOI : 10.1145/1557019.1557129

T. Kanamori and T. Takenouchi, Improving Logitboost with prior knowledge, Information Fusion, vol.14, issue.2, pp.208-219, 2013.
DOI : 10.1016/j.inffus.2011.11.004

W. Wang, Y. Wang, F. Chen, and A. Sowmya, A weakly supervised approach for object detection based on Soft-Label Boosting, 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp.331-338, 2013.
DOI : 10.1109/WACV.2013.6475037

A. Jeff and . Bilmes, A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models, International Computer Science Institute, vol.4, issue.510, p.126, 1998.

Z. Zhou and M. Li, Tri-training: Exploiting unlabeled data using three classifiers. Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.11, pp.1529-1541, 2005.

P. Gabbert, Decision trees from uncertain learning sets, Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp.913-918, 1994.
DOI : 10.1109/ICSMC.1994.399953

L. Breiman, J. Friedman, J. Charles, . Stone, A. Richard et al., Classification and regression trees, 1984.

D. Reynolds, Gaussian mixture models. Encyclopedia of Biometrics, pp.659-663, 2009.

A. Richard, . Redner, F. Homer, and . Walker, Mixture densities, maximum likelihood and the em algorithm, SIAM review, vol.26, issue.2, pp.195-239, 1984.

D. Gerónimo, D. Angel, D. Sappa, . Ponsa, M. Antonio et al., 2D???3D-based on-board pedestrian detection system, Computer Vision and Image Understanding, vol.114, issue.5, pp.583-595, 2010.
DOI : 10.1016/j.cviu.2009.07.008

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011.
DOI : 10.1145/1961189.1961199

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, Three-dimensional scene flow, Computer Vision The Proceedings of the Seventh IEEE International Conference on, pp.722-729, 1999.

Y. Zhang and C. Kambhamettu, Integrated 3D scene flow and structure recovery from multiview image sequences, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.674-681, 2000.
DOI : 10.1109/CVPR.2000.854939

C. Vogel, K. Schindler, and S. Roth, 3D scene flow estimation with a rigid motion prior, 2011 International Conference on Computer Vision, pp.1291-1298, 2011.
DOI : 10.1109/ICCV.2011.6126381

C. Vogel, S. Roth, and K. Schindler, View-Consistent 3D Scene Flow Estimation over Multiple Frames, Computer Vision?ECCV 2014, pp.263-278, 2014.
DOI : 10.1007/978-3-319-10593-2_18

D. Michael, F. Breitenstein, B. Reichlin, E. Leibe, L. Koller-meier et al., Robust tracking-by-detection using a detector confidence particle filter, Computer Vision IEEE 12th International Conference on, pp.1515-1522, 2009.

P. Smets, The transferable belief model and random sets, International Journal of Intelligent Systems, vol.2, issue.1, pp.37-46, 1992.
DOI : 10.1002/int.4550070106

Z. Elouedi, K. Mellouli, and P. Smets, Belief decision trees: theoretical foundations, International Journal of Approximate Reasoning, vol.28, issue.2-3, pp.91-124, 2001.
DOI : 10.1016/S0888-613X(01)00045-7

B. Quost and T. Denoeux, Learning from data with uncertain labels by boosting credal classifiers, Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U '09, pp.38-47, 2009.
DOI : 10.1145/1610555.1610561

URL : https://hal.archives-ouvertes.fr/hal-00445491

A. Martin and C. Osswald, Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, pp.1-6, 2008.
DOI : 10.1109/ICTTA.2008.4530035

URL : https://hal.archives-ouvertes.fr/hal-00286533

A. Martin, Reliability and combination rule in the theory of belief functions, Information Fusion FUSION'09. 12th International Conference on, pp.529-536, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00518546

A. Martin, C. Osswald, J. Dezert, and F. Smarandache, General combination rules for qualitative and quantitative beliefs, Journal of Advances in Information Fusion, vol.3, issue.2, pp.67-82, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00399662

D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), pp.7-42, 2002.
DOI : 10.1109/SMBV.2001.988771

Z. Myron, D. Brown, . Burschka, D. Gregory, and . Hager, Advances in computational stereo. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.8, pp.993-1008, 2003.

M. Steven, B. Seitz, J. Curless, D. Diebel, R. Scharstein et al., A comparison and evaluation of multi-view stereo reconstruction algorithms, Computer vision and pattern recognition, pp.519-528, 2006.

Z. Wang and Z. Zheng, A region based stereo matching algorithm using cooperative optimization, Computer Vision and Pattern Recognition CVPR 2008. IEEE Conference on, pp.1-8, 2008.

E. Tola, V. Lepetit, and P. Fua, A fast local descriptor for dense matching, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587673

K. Zhang, J. Lu, and G. Lafruit, Cross-based local stereo matching using orthogonal integral images. Circuits and Systems for Video Technology, IEEE Transactions on, vol.19, issue.7, pp.1073-1079, 2009.

A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann, Local stereo matching using geodesic support weights, 2009 16th IEEE International Conference on Image Processing (ICIP), pp.2093-2096, 2009.
DOI : 10.1109/ICIP.2009.5414478

G. Van-meerbergen, M. Vergauwen, M. Pollefeys, and L. Van-gool, A hierarchical symmetric stereo algorithm using dynamic programming, International Journal of Computer Vision, vol.47, issue.1/3, pp.275-285, 2002.
DOI : 10.1023/A:1014562312225

M. Bleyer and M. Gelautz, Simple but effective tree structures for dynamic programming-based stereo matching, pp.415-422, 2008.

V. Kolmogorov and R. Zabih, Computing visual correspondence with occlusions using graph cuts, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.508-515, 2001.
DOI : 10.1109/ICCV.2001.937668

URL : http://ce.sharif.edu/~elno/disparitymap/Papers/KZ-ICCV01.pdf

L. Hong and G. Chen, Segment-based stereo matching using graph cuts In Computer Vision and Pattern Recognition, Proceedings of the 2004 IEEE Computer Society Conference on, p.74, 2004.

J. Sun, N. Zheng, and H. Shum, Stereo matching using belief propagation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.7, pp.787-800, 2003.

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.3, pp.31492-504, 2009.

K. Stefan, C. Gehrig, and . Rabe, Real-time semi-global matching on the cpu, Computer Vision and Pattern Recognition Workshops (CVPRW), pp.85-92, 2010.

Q. Yang, L. Wang, R. Yang, S. Wang, M. Liao et al., Real-time Global Stereo Matching Using Hierarchical Belief Propagation, Procedings of the British Machine Vision Conference 2006, pp.989-998, 2006.
DOI : 10.5244/C.20.101

M. Humenberger, C. Zinner, M. Weber, W. Kubinger, and M. Vincze, A fast stereo matching algorithm suitable for embedded real-time systems, Computer Vision and Image Understanding, vol.114, issue.11, pp.1180-1202, 2010.
DOI : 10.1016/j.cviu.2010.03.012

K. Berthold, B. G. Horn, and . Schunck, Determining optical flow, Artificial intelligence, vol.17, pp.185-203, 1981.

L. John, . Barron, J. David, . Fleet, S. Steven et al., Performance of optical flow techniques, International journal of computer vision, vol.12, issue.1, pp.43-77, 1994.

S. Steven, J. L. Beauchemin, and . Barron, The computation of optical flow, ACM Computing Surveys (CSUR), vol.27, issue.3, pp.433-466, 1995.

J. Weickert, A. Bruhn, T. Brox, and N. Papenberg, A Survey on Variational Optic Flow Methods for Small Displacements, Mathematical models for registration and applications to medical imaging, pp.103-136, 2006.
DOI : 10.1007/978-3-540-34767-5_5

J. Bouguet, Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm, Intel Corporation, vol.2, issue.3, 2001.

C. Zach, T. Pock, and H. Bischof, A Duality Based Approach for Realtime TV-L 1 Optical Flow, Pattern Recognition, pp.214-223, 2007.
DOI : 10.1007/978-3-540-74936-3_22

D. Sun, S. Roth, J. Michael, and . Black, Secrets of optical flow estimation and their principles, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2432-2439, 2010.
DOI : 10.1109/CVPR.2010.5539939

S. Baker, D. Scharstein, S. Lewis, . Roth, J. Michael et al., A Database and Evaluation Methodology for Optical Flow, International Journal of Computer Vision, vol.27, issue.3, pp.1-31, 2011.
DOI : 10.1007/s11263-010-0390-2

L. Di-stefano, S. Mattoccia, and F. Tombari, ZNCC-based template matching using bounded partial correlation, Pattern Recognition Letters, vol.26, issue.14, pp.2129-2134, 2005.
DOI : 10.1016/j.patrec.2005.03.022