FREAK: Fast Retina Keypoint, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.510-517, 2012. ,
DOI : 10.1109/CVPR.2012.6247715
Color-based road detection in urban traffic scenes. Intelligent Transportation Systems, IEEE Transactions on, vol.5, issue.4, pp.309-318, 2004. ,
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
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
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
Traffic sign recognition and analysis for intelligent vehicles, Image and vision computing, vol.21, issue.3, pp.247-258, 2003. ,
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
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. ,
On-road vehicle detection: A review. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.28, issue.5, pp.694-711, 2006. ,
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. ,
Rigid Motion Segmentation Using Randomized Voting, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.158
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. ,
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
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
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
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
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. ,
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
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
Skeletal graphs for efficient structure from motion, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. ,
DOI : 10.1109/CVPR.2008.4587678
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
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
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
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. ,
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
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
Moving object detection in spatial domain using background removal techniques-stateof-art . Recent patents on computer science, pp.32-54, 2008. ,
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
Multiple view geometry in computer vision, 2003. ,
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
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
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. ,
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. ,
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
Global stereo matching leveraged by sparse ground control points, CVPR 2011, pp.3033-3040, 2011. ,
DOI : 10.1109/CVPR.2011.5995480
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
A spectral clustering and kalman filtering based objects detection and tracking REFERENCES 123 ,
Objects Detection and Tracking Using Points Cloud Reconstructed from Linear Stereo Vision, CURRENT ADVANCEMENTS IN STEREO VISION, p.161, 2012. ,
DOI : 10.5772/46026
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
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
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
Stereo scene flow for 3D motion analysis, 2011. ,
DOI : 10.1007/978-0-85729-965-9
Beyond pixel, Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, ICIMCS '15, 2009. ,
DOI : 10.1145/2808492.2808573
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
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
Learning OpenCV: Computer vision with the OpenCV library, 2008. ,
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
Propagating covariance in computer vision, Performance Characterization in Computer Vision, pp.95-114, 2000. ,
Modelling uncertainty: A primer, p.216198, 1998. ,
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. ,
On the generalised distance in statistics, Proceedings of the National Institute of Science of India, pp.49-55, 1936. ,
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. ,
"GrabCut", ACM Transactions on Graphics, vol.23, issue.3, pp.309-314, 2004. ,
DOI : 10.1145/1015706.1015720
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. ,
Automatic segmentation fusing color and depth, Pattern Recognition (ICPR), 21st International Conference on, pp.763-766, 2012. ,
Exact maximum a posteriori estimation for binary images, Journal of the Royal Statistical Society. Series B (Methodological), pp.271-279, 1989. ,
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
UV-disparity: an efficient algorithm for stereovision based scene analysis, Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pp.48-54, 2005. ,
Putting Objects in Perspective, International Journal of Computer Vision, vol.57, issue.2, pp.3-15, 2008. ,
DOI : 10.1007/s11263-008-0137-5
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
Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, issue.11, p.2013 ,
DOI : 10.1177/0278364913491297
Beyond pixel, Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, ICIMCS '15, 2009. ,
DOI : 10.1145/2808492.2808573
An iterative image registration technique with an application to stereo vision, IJCAI, pp.674-679, 1981. ,
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
Motion segmentation of multiple objects from a freely moving monocular camera, Robotics and Automation (ICRA), 2012 IEEE International Conference on, pp.4092-4099, 2012. ,
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
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
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
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. ,
Ten Years of Pedestrian Detection, What Have We Learned?, ECCV, CVRSUAD workshop, 2014. ,
DOI : 10.1007/978-3-319-16181-5_47
A trainable pedestrian detection system, Proceeding of Intelligent Vehicles, pp.241-246, 1998. ,
Adaptive image sampling and windows classification for on-board pedestrian detection, Proceedings of the International Conference on Computer Vision Systems, 2007. ,
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
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
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. ,
Distinctive image features from scale-invariant keypoints, International journal of computer vision, vol.60, issue.2, pp.91-110, 2004. ,
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
Object recognition from local scale-invariant features The proceedings of the seventh IEEE, Computer vision, pp.1150-1157, 1999. ,
Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions, vol.24, issue.4, pp.509-522, 2002. ,
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
Contextual boost for pedestrian detection, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.2895-2902, 2012. ,
Putting objects in perspective, Computer Vision and Pattern Recognition, pp.2137-2144, 2006. ,
Using segmentation to verify object hypotheses In Computer Vision and Pattern Recognition, CVPR'07. IEEE Conference on, pp.1-8, 2007. ,
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
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
Example-based object detection in images by components. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.23, issue.4, pp.349-361, 2001. ,
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
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
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
Object detection with discriminatively trained part-based models . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.9, pp.1627-1645, 2010. ,
Dimensionality reduction: A comparative review, Journal of Machine Learning Research, vol.10, pp.1-4166, 2009. ,
Principal component analysis, 2005. ,
DOI : 10.1007/978-1-4757-1904-8
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. ,
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
Robust head-shoulder detection by pcabased multilevel hog-lbp detector for people counting, Pattern Recognition (ICPR), 2010 International Conference on, pp.2069-2072, 2010. ,
A short introduction to boosting, Journal-Japanese Society For Artificial Intelligence, vol.14, pp.771-7801612, 1999. ,
Improved boosting algorithms using confidencerated predictions, Machine Learning, vol.37, issue.3, pp.297-336, 1999. ,
DOI : 10.1023/A:1007614523901
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
Floatboost learning and statistical face detection . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, issue.9, pp.1112-1123, 2004. ,
Survey on boosting algorithms for supervised and semi-supervised learning, 2007. ,
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
Fast human detection using a cascade of histograms of oriented gradients, Computer Vision and Pattern Recognition, pp.1491-1498, 2006. ,
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
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
The nature of statistical learning theory, 2000. ,
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
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
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
A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, pp.144-152, 1992. ,
Pedestrian detection with convolutional neural networks, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.224-229, 2005. ,
DOI : 10.1109/IVS.2005.1505106
An experimental study on pedestrian classification . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.28, pp.1863-1868, 2006. ,
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
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
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
A discriminative deep model for pedestrian detection with occlusion handling, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.3258-3265, 2012. ,
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
Joint Deep Learning for Pedestrian Detection, 2013 IEEE International Conference on Computer Vision, pp.2056-2063, 2013. ,
DOI : 10.1109/ICCV.2013.257
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
Semi-supervised learning, 2006. ,
DOI : 10.7551/mitpress/9780262033589.001.0001
Semi-supervised learning literature survey, 2006. ,
Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.3, issue.1, 2009. ,
DOI : 10.2200/S00196ED1V01Y200906AIM006
Transductive inference for text classification using support vector machines, ICML, pp.200-209, 1999. ,
Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning, COLT, pp.33-44, 2008. ,
Statistical analysis of semi-supervised regression, Advances in Neural Information Processing Systems, pp.801-808, 2007. ,
Boosting with prior knowledge for call classification. Speech and Audio Processing, IEEE Transactions on, vol.13, pp.174-181, 2005. ,
SERBoost: Semi-supervised Boosting with Expectation Regularization, Computer Vision-ECCV 2008, pp.588-601, 2008. ,
DOI : 10.1007/978-3-540-88690-7_44
Semiboost: Boosting for semi-supervised learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.11, pp.312000-2014, 2009. ,
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. ,
Regularized boost for semi-supervised learning, Advances in neural information processing systems, pp.281-288, 2008. ,
Functional gradient techniques for combining hypotheses, ADVANCES IN NEU- RAL INFORMATION PROCESSING SYSTEMS, pp.221-246, 1999. ,
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
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
Improving Logitboost with prior knowledge, Information Fusion, vol.14, issue.2, pp.208-219, 2013. ,
DOI : 10.1016/j.inffus.2011.11.004
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 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. ,
Tri-training: Exploiting unlabeled data using three classifiers. Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.11, pp.1529-1541, 2005. ,
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
Classification and regression trees, 1984. ,
Gaussian mixture models. Encyclopedia of Biometrics, pp.659-663, 2009. ,
Mixture densities, maximum likelihood and the em algorithm, SIAM review, vol.26, issue.2, pp.195-239, 1984. ,
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
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011. ,
DOI : 10.1145/1961189.1961199
Three-dimensional scene flow, Computer Vision The Proceedings of the Seventh IEEE International Conference on, pp.722-729, 1999. ,
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
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
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
Robust tracking-by-detection using a detector confidence particle filter, Computer Vision IEEE 12th International Conference on, pp.1515-1522, 2009. ,
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
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
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
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
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
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
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
Advances in computational stereo. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.8, pp.993-1008, 2003. ,
A comparison and evaluation of multi-view stereo reconstruction algorithms, Computer vision and pattern recognition, pp.519-528, 2006. ,
A region based stereo matching algorithm using cooperative optimization, Computer Vision and Pattern Recognition CVPR 2008. IEEE Conference on, pp.1-8, 2008. ,
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
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. ,
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
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
Simple but effective tree structures for dynamic programming-based stereo matching, pp.415-422, 2008. ,
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
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. ,
Stereo matching using belief propagation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.7, pp.787-800, 2003. ,
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. ,
Real-time semi-global matching on the cpu, Computer Vision and Pattern Recognition Workshops (CVPRW), pp.85-92, 2010. ,
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
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
Determining optical flow, Artificial intelligence, vol.17, pp.185-203, 1981. ,
Performance of optical flow techniques, International journal of computer vision, vol.12, issue.1, pp.43-77, 1994. ,
The computation of optical flow, ACM Computing Surveys (CSUR), vol.27, issue.3, pp.433-466, 1995. ,
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
Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm, Intel Corporation, vol.2, issue.3, 2001. ,
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
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
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
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