W. A. Abdulhafiz and A. Khamis, Bayesian approach to multisensor data fusion with Pre- and Post-Filtering, 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), pp.373-378, 2013.
DOI : 10.1109/ICNSC.2013.6548766

. Alameda-pineda, J. Xavier, R. Staiano, L. M. Subramanian, E. Batrinca et al., SALSA: A Novel Dataset for Multimodal Group Behavior Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.8, 2015.
DOI : 10.1109/TPAMI.2015.2496269

A. Amditis and G. Thomaidis, Multiple Hypothesis Tracking Implementation, Pantelis Maroudis, Panagiotis Lytrivis, and Giannis Karaseitanidis InTech. Chap, 2012.
DOI : 10.5772/33583
URL : http://www.intechopen.com/articles/show/title/multiple-hypothesis-tracking-implementation

M. Arulampalam, S. Sanjeev, N. Maskell, T. Gordon, and . Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing 50.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

S. Avidan, Support Vector Tracking''. In: Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference on, 2001.

D. Baltieri, R. Vezzani, and R. Cucchiara, 3DPeS, Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, J-HGBU '11, pp.59-64, 2011.
DOI : 10.1145/2072572.2072590

Y. Bar-shalom and E. Tse, Tracking in a cluttered environment with probabilistic data association, Automatica, vol.11, issue.5, pp.451-460, 1975.
DOI : 10.1016/0005-1098(75)90021-7

L. Bazzani, D. Bloisi, and V. Murino, A Comparison of Multi Hypothesis Kalman Filter and Particle Filter for Multi-target Tracking''. In: Performance Evaluation of Tracking and Surveillance workshop at CVPR, pp.47-54, 2009.

. Berclaz, . J\-'er\^ome, E. Fran\c-cois-fleuret, P. T\-"-uretken, and . Fua, Multiple Object Tracking Using K-Shortest Paths Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.9, pp.1806-1819, 2011.
DOI : 10.1109/TPAMI.2011.21

M. Bertalmio, G. Sapiro, and G. Randall, Morphing active contours: a geometric approach to topology-independent image segmentation and tracking, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), pp.318-322, 1998.
DOI : 10.1109/ICIP.1998.999021

M. Bertozzi, A. Broggi, A. Fascioli, T. Graf, and M. Meinecke, Pedestrian Detection for Driver Assistance Using Multiresolution Infrared Vision, IEEE Transactions on Vehicular Technology 53.6, pp.1666-1678, 2004.
DOI : 10.1109/TVT.2004.834878

A. Bevilacqua, L. D. Stefano, and S. Vaccari, Occlusion Robust Vehicle Tracking based on SOM (Self-Organizing Map), 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, pp.84-89, 2005.
DOI : 10.1109/ACVMOT.2005.87

M. J. Black and A. D. Jepson, EigenTracking: Robust matching and tracking of articulated objects using a view-based representation, International Journal of Computer Vision, vol.261, pp.63-841007939232436, 1998.
DOI : 10.1007/BFb0015548

S. S. Blackman, Multiple hypothesis tracking for multiple target tracking, IEEE Aerospace and Electronic Systems Magazine, vol.19, issue.1, pp.5-18, 2004.
DOI : 10.1109/MAES.2004.1263228

A. Bohlooli and K. Jamshidi, A GPS-free method for vehicle future movement directions prediction using SOM for VANET, Applied Intelligence, vol.11, issue.3, pp.685-697, 2012.
DOI : 10.1007/s10489-011-0289-9

A. Bonarini, W. Burgard, G. Fontana, M. Matteucci, D. G. Sorrenti et al., RAWSEEDS: Robotics Advancement through Web-publishing of Sensorial and Elaborated Extensive Data Sets, proceedings of IROS'06 Workshop on Benchmarks in Robotics Research, 2006.

S. Bouaziz, V\'ehicule automatique -\'Etude technique, p.89, 2013.

J. Bouguet, Camera Calibration Toolbox for Matlab. url: http://robots, 2003.

G. Bradski, OpenCV tools, Dr. Dobb's Journal of Software Tools, 2000.

. Br\k-s\v-ci\-'c, T. Dra\v-zen, T. Kanda, T. Ikeda, and . Miyashita, Person position and body direction tracking in large public spaces using 3D range sensors, IEEE Transactions on Human-Machine Systems, 2013.

M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder et al., The EuRoC micro aerial REFERENCES vehicle datasets, The International Journal of Robotics Research, 2016.

Z. Cai, J. Han, L. Liu, and L. Shao, RGB-D datasets using microsoft kinect or similar sensors: a survey, Multimedia Tools and Applications, pp.1-43, 2016.
DOI : 10.1007/s11042-016-3374-6

C. Caraffi, T. Vojir, and J. Trefny, A system for real-time detection and tracking of vehicles from a single car-mounted camera, 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp.975-982, 2012.
DOI : 10.1109/ITSC.2012.6338748

L. Caron, Y. Song, D. Filliat, and A. Gepperth, Neural network based 2D/3D fusion for robotic object recognition, Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (cit, p.34, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01012090

F. Castanedo, A Review of Data Fusion Techniques''. en In: The Scientific World Journal 2013, p.32, 2013.

S. Ceriani, G. Fontana, A. Giusti, D. Marzorati, M. Matteucci et al., Rawseeds ground truth collection systems for indoor self-localization and mapping, Autonomous Robots, vol.4, issue.4, pp.353-371, 2009.
DOI : 10.1007/s10514-009-9156-5

T. Chang and S. Gong, Tracking multiple people with a multi-camera system, Proceedings 2001 IEEE Workshop on Multi-Object Tracking, pp.19-26, 2001.
DOI : 10.1109/MOT.2001.937977

R. Chapuis, R. Aufrere, and F. , Accurate road following and reconstruction by computer vision, IEEE Transactions on Intelligent Transportation Systems 3.4, pp.261-270, 2002.
DOI : 10.1109/TITS.2002.804751

R. Chavez-garcia and . Omar, Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01082021

F. Cheikh, S. Alaya, V. Kumar-saha, P. Rudakova, and . Wang, Multipeople tracking across multiple cameras, International Journal of New Computer Architectures and their Applications, vol.2, pp.23-33, 2012.

Y. Cheng and T. Singh, Efficient particle filtering for road-constrained target tracking, IEEE Transactions on Aerospace and Electronic Systems, vol.434, pp.1454-1469, 2007.

J. Cho, J. C. Principe, D. Erdogmus, and M. A. Motter, Modeling and Inverse Controller Design for an Unmanned Aerial Vehicle Based on the Self-Organizing Map, IEEE Transactions on Neural Networks 17.2, pp.445-460, 2006.
DOI : 10.1109/TNN.2005.863422

N. Chumerin and M. M. Van-hulle, Cue and Sensor Fusion for Independent Moving Objects Detection and Description in Driving Scenes, pp.161-180, 2008.
DOI : 10.1007/978-0-387-74367-7_9
URL : https://lirias.kuleuven.be/bitstream/123456789/271357/3/Chumerin-2008-SPTKEIF-chapter.pdf

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

D. Cox and D. G. Mayo, Objectivity and Conditionality in Frequentist Inference, Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, 2010.

I. J. Cox, A review of statistical data association techniques for motion correspondence, International Journal of Computer Vision, vol.7, issue.3, 1992.
DOI : 10.1007/BF01440847

H. Cramer, U. Scheunert, and G. Wanielik, Multi sensor fusion for object detection using generalized feature models, Sixth International Conference of Information Fusion, 2003. Proceedings of the, 2003.
DOI : 10.1109/ICIF.2003.177419

J. L. Crowley, Principles and Techniques for Sensor Data Fusion In: Multisensor Fusion for Computer Vision, pp.15-36, 1993.

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), 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

M. Darms, P. Rybski, and C. Urmson, A Multisensor Multiobject Tracking System for an Autonomous Vehicle Driving in an Urban Environment, AVEC 2008 -9th International Symposium on Advanced Vehicle Control. Kobe (cit, p.17, 2008.

J. W. Davis and V. Sharma, Background-subtraction using contour-based fusion of thermal and visible imagery, Computer Vision and Image Understanding, vol.106, issue.2-3, pp.162-182, 2007.
DOI : 10.1016/j.cviu.2006.06.010

T. Denoeux, The cautious rule of combination for belief functions and some extensions, 2006 9th International Conference on Information Fusion, pp.1-8, 2006.
DOI : 10.1109/ICIF.2006.301572

A. Derder and S. Moussaoui, Target Tracking in VANETs Using V2I and V2V Communication, Advanced Networking Distributed Systems and Applications (INDS), 2014 International Conference on, pp.19-24, 2014.

A. Derder, S. Moussaoui, and A. Boualouache, Target tracking in VANETs using V2V communication with packet load enhancement, 2015 First International Conference on New Technologies of Information and Communication (NTIC), pp.1-6, 2015.
DOI : 10.1109/NTIC.2015.7368738

E. Dickmanns and . Dieter, An Advanced Vision System for Ground Vehicles, International Workshop on In-Vehicle Cognitive Computer Vision Systems (IVC2VS) (cit, p.25, 2003.
DOI : 10.1504/ijvas.2002.001804

K. Dixon, StarFire: A Global SBAS for Sub-Decimeter Precise Point Positioning, 2006.

. Doll\-'ar, C. Piotr, B. Wojek, P. Schiele, and . Perona, Pedestrian Detection: A Benchmark, CVPR (cit, p.85, 2009.

S. Dubuisson and C. Gonzales, A survey of datasets for visual tracking, Machine Vision and Applications, vol.46, issue.7, pp.23-52, 2016.
DOI : 10.1007/s00138-015-0713-y
URL : https://hal.archives-ouvertes.fr/hal-01217152

J. Esteban, A. Starr, R. Willetts, P. Hannah, and P. Bryanston-cross, A Review of data fusion models and architectures: towards engineering guidelines, Neural Computing and Applications, vol.6, issue.6, 2005.
DOI : 10.1007/s00521-004-0463-7

S. E. Fienberg, When did Bayesian inference become "Bayesian"?, Bayesian Analysis, vol.1, issue.1, pp.1-41, 2006.
DOI : 10.1214/06-BA101
URL : http://ba.stat.cmu.edu/journal/2006/vol01/issue01/fienberg.pdf

F. Fleuret, . Cois, R. J\-'er\^ome-berclaz, P. Lengagne, and . Fua, Multicamera People Tracking with a Probabilistic Occupancy Map, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.2, pp.267-282, 2008.
DOI : 10.1109/TPAMI.2007.1174
URL : http://infoscience.epfl.ch/record/145991

V. Fremont, S. A. , R. Florez, and P. Bonnifait, Circular Targets for 3D Alignment of Video and Lidar Sensors, Advanced Robotics, vol.41, issue.3, pp.2087-2113, 2012.
DOI : 10.1080/01691864.2012.703235
URL : https://hal.archives-ouvertes.fr/hal-00858103

J. Fritsch, T. Kuehnl, and A. Geiger, A new performance measure and evaluation benchmark for road detection algorithms, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp.1693-1700, 2013.
DOI : 10.1109/ITSC.2013.6728473

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, pp.1231-1237, 2013.
DOI : 10.1177/0278364913491297

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, F. Moosmann, O. Car, and B. Schuster, Automatic camera and range sensor calibration using a single shot, 2012 IEEE International Conference on Robotics and Automation, p.39, 2012.
DOI : 10.1109/ICRA.2012.6224570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.229.1802

A. Gepperth, M. G. Ortiz, E. Sattarov, and B. Heisele, Dynamic attention priors: a new and efficient concept for improving object detection, Neurocomputing, vol.197, issue.123, pp.14-28, 2016.
DOI : 10.1016/j.neucom.2016.01.036
URL : https://hal.archives-ouvertes.fr/hal-01418128

A. Gepperth, E. Sattarov, B. Heisele, and S. Flores, Robust visual pedestrian detection by tight coupling to tracking, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1935-1940, 2014.
DOI : 10.1109/ITSC.2014.6957989
URL : https://hal.archives-ouvertes.fr/hal-01098703

M. Ghahroudi, A. Rezaei, and . Fasih, A Hybrid Method in Driver and Multisensor Data Fusion, Using a Fuzzy Logic Supervisor for Vehicle Intelligence, 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), 2007.
DOI : 10.1109/SENSORCOMM.2007.4394953

E. Goubet, J. Katz, and F. Porikli, Pedestrian tracking using thermal infrared imaging, Infrared Technology and Applications XXXII, 2006.
DOI : 10.1117/12.673132

B. Grandbois and F. Pauling, Driver node for Sick LD-MRS. url: http, 2012.

F. Gustafsson, Statistical signal processing for automotive safety systems, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, pp.1428-1435, 2005.
DOI : 10.1109/SSP.2005.1628820

. Habtemariam, K. Biruk, R. Tharmarasa, T. Thayaparan, M. Mallick et al., A Multiple-Detection Joint Probabilistic REFERENCES Data Association Filter, IEEE Journal of Selected Topics in Signal Processing, vol.73, pp.461-471, 2013.
DOI : 10.1109/jstsp.2013.2256772

G. Halfacree and E. Upton, Raspberry Pi User Guide. 1st, p.9781118464465, 2012.

D. Hall, M. E. Lee, J. Liggins, and . Llinas, Handbook of multisensor data fusion : theory and practice. English, Boca Raton, vol.33, pp.27487296-60, 2001.

E. Hassan, G. Shroff, and P. Agarwal, Multi-sensor event detection using shape histograms, Proceedings of the Second ACM IKDD Conference on Data Sciences, CoDS '15, pp.20-29, 2015.
DOI : 10.1145/2732587.2732591
URL : http://arxiv.org/abs/1408.3733

D. Held, J. Levinson, and S. Thrun, Precision tracking with sparse 3D and dense color 2D data, 2013 IEEE International Conference on Robotics and Automation, pp.1138-1145, 2013.
DOI : 10.1109/ICRA.2013.6630715

Z. Hendzel, Collision free path planning and control of wheeled mobile robot using Kohonen self-organising map, p.61, 2005.

L. Heng, B. Li, and M. Pollefeys, CamOdoCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1793-1800, 2013.
DOI : 10.1109/IROS.2013.6696592

C. Hermes, C. W\-"-ohler, K. Schenk, and F. Kummert, Longterm Vehicle Motion Prediction, IEEE Intelligent Vehicles Symposium. Xi'an, China, pp.652-657, 2009.
DOI : 10.1109/ivs.2009.5164354
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.9014

T. Heskes, Energy functions for self-organizing maps, 1999.
DOI : 10.1016/B978-044450270-4/50024-3
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.6572

R. S. Holt, A. Peter, E. K. Mastromarino, M. B. Kao, and . Hurley, Information theoretic approach for performance evaluation of multi-class assignment systems, Signal Processing, Sensor Fusion, and Target Recognition XIX, 2010.
DOI : 10.1117/12.851019

S. Hsu, S. Acharya, A. Rafii, and R. New, Performance of a Time-of-Flight Range Camera for Intelligent Vehicle Safety Applications, pp.205-219, 2006.
DOI : 10.1007/3-540-33410-6_16

T. Hu, S. Messelodi, and O. Lanz, Dynamic task decomposition for decentralized object tracking in complex scenes, Computer Vision and Image Understanding, vol.134, pp.89-104, 2015.
DOI : 10.1016/j.cviu.2015.02.007

T. Hu, S. Mutlu, and O. Lanz, Multicamera People Tracking Using a Locusbased Probabilistic Occupancy Map, Image Analysis and Processing --ICIAP 2013: 17th International Conference Proceedings, Part II, 2013.
DOI : 10.1007/978-3-642-41184-7_70

X. Hu, S. A. Rodr\-'\i-guez-florez, and A. Gepperth, A multi-modal system for road detection and segmentation, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp.1365-1370, 2014.
DOI : 10.1109/IVS.2014.6856466
URL : https://hal.archives-ouvertes.fr/hal-01023615

Z. Hu, Y. Jin, J. Li, and X. Liu, Maneuvering Target Tracking Algorithm Based on Multiple Model Rao-Blackwellised Particle Filter, Journal of Information and Computational Science, vol.8, 2012.

J. Humphreys and A. Hunter, Multiple object tracking using a neural cost function, Image and Vision Computing 27.4, pp.417-424, 2009.
DOI : 10.1016/j.imavis.2008.06.002
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1026.4024

G. International and . Service, RINEX Working Group and Radio Technical Commission for Maritime Services Special Committee 104 (RTCM-SC104) (2013). RINEX The Receiver Independent Exchange Format. version 3.02. url: ftp://igs

M. Isard and A. Blake, Conditional Density Propagation for Visual Tracking, International Journal of Computer Vision, vol.291, pp.5-28, 1998.

L. Jaechan, The Joint Probabilistic Data Association Filter (JPDAF) for Multi-Target Tracking, p.74, 2006.

O. Jafari, D. Hosseini, B. Mitzel, and . Leibe, Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.5636-5643, 2014.
DOI : 10.1109/ICRA.2014.6907688

A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, pp.0-13, 1988.

O. Javed, Z. Rasheed, K. Shafique, and M. Shah, Tracking across multiple cameras with disjoint views, Proceedings Ninth IEEE International Conference on Computer Vision, pp.952-957, 2003.
DOI : 10.1109/ICCV.2003.1238451

Z. Kalal, K. Mikolajczyk, and J. Matas, Tracking-Learning-Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, 2012.
DOI : 10.1109/TPAMI.2011.239

J. Kang, I. Cohen, and G. Medioni, Object Reacquisition Using Invariant Appearance Model, Proceedings of the Pattern Recognition, 17th International Conference on, pp.759-762, 2004.

E. Kaplan, Understanding GPS -Principles and applications, 2005.

N. Katsarakis, F. Talantzis, A. Pnevmatikakis, and L. Polymenakos, Multimodal Technologies for Perception of Humans In: International Evaluation Workshops CLEAR Revised Selected Papers Chap. The AIT 3D Audio / Visual Person Tracker for CLEAR, pp.35-46, 2007.

C. Keller, M. Gustav, D. M. Enzweiler, and . Gavrila, A new benchmark for stereo-based pedestrian detection, 2011 IEEE Intelligent Vehicles Symposium (IV), p.86, 2011.
DOI : 10.1109/IVS.2011.5940480
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2909

N. Khairdoost, S. A. Monadjemi, and K. Jamshidi, Front and Rear Vehicle Detection Using Hypothesis Generation and Verification, Signal \& Image Processing, pp.31-50, 2013.
DOI : 10.5121/sipij.2013.4403

C. Kim, F. Li, A. Ciptadi, and J. M. Rehg, Multiple Hypothesis Tracking Revisited ICCV '15, Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp.4696-4704, 2015.
DOI : 10.1109/iccv.2015.533

K. Kim and L. S. Davis, Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering, Computer Vision --ECCV 2006: 9th European Conference on Computer Vision Proceedings, Part III, pp.98-109, 2006.
DOI : 10.1007/11744078_8

A. Klausne, A. Tengg, and B. Rinner, Vehicle Classification on Multi-Sensor Smart Cameras Using Feature- and Decision-Fusion, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, pp.67-74, 2007.
DOI : 10.1109/ICDSC.2007.4357507

D. A. Klein, BoBot -Bonn benchmark on tracking, 2010.

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

A. Kolb, E. Barth, R. Koch, and R. Larsen, Time-of-Flight Cameras in Computer Graphics, Computer Graphics Forum, vol.418, issue.6900, pp.141-159, 2010.
DOI : 10.1111/j.1467-8659.2009.01583.x

P. Konstantinova, A. Udvarev, and T. Semerdjiev, A study of a target tracking algorithm using global nearest neighbor approach, Proceedings of the 4th international conference conference on Computer systems and technologies e-Learning, CompSysTech '03, pp.290-295, 2003.
DOI : 10.1145/973620.973668

J. F. Kooij, N. Schneider, and D. M. Gavrila, Analysis of pedestrian dynamics from a vehicle perspective, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp.1445-1450, 2014.
DOI : 10.1109/IVS.2014.6856505

P. Koschorrek, T. Piccini, \. Per, M. Oberg, L. Felsberg et al., A Multi-sensor Traffic Scene Dataset with Omnidirectional Video, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.727-734, 2013.
DOI : 10.1109/CVPRW.2013.110

K. K\-"-uhnlenz, Aspects of Multi-focal Vision Fortschrittberichte VDI / 8. VDI-Verlag. isbn: 9783185129087. url: https://books.google.fr/books?id=pzaEMwAACAAJ (cit, p.25, 2007.

. Leal-taix\-'e, A. Laura, I. Milan, S. Reid, K. Roth et al., MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking, 2015.

J. Levinson and S. Thrun, Automatic Online Calibration of Cameras and Lasers, Robotics: Science and Systems IX, 2013.
DOI : 10.15607/RSS.2013.IX.029

X. Li, M. Larson, and A. Hanjalic, Pairwise geometric matching for largescale object retrieval, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5153-5161, 2015.

T. Linder, S. Breuers, B. Leibe, and K. O. Arras, On multi-modal people tracking from mobile platforms in very crowded and dynamic environments, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.5512-5519, 2016.
DOI : 10.1109/ICRA.2016.7487766

F. Liu, J. Sparbert, and C. Stiller, IMMPDA vehicle tracking system using asynchronous sensor fusion of radar and vision''. In: Intelligent Vehicles Symposium, IEEE, pp.168-173, 2008.

J. Llinas and D. L. Hall, An introduction to multi-sensor data fusion, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187), pp.537-540, 1998.
DOI : 10.1109/ISCAS.1998.705329

B. D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence -Volume 2. IJCAI'81, pp.674-679, 1981.

T. Luettel, M. Himmelsbach, and H. Wuensche, Autonomous Ground Vehicles 2014;Concepts and a Path to the Future, Proceedings of the IEEE 100.Special Centennial Issue, pp.1831-1839, 2012.

R. C. Luo, K. Chih-chen-yih, and . Su, Multisensor fusion and integration: approaches, applications, and future research directions, IEEE Sensors Journal 2.2, pp.107-119, 2002.
DOI : 10.1109/JSEN.2002.1000251

B. Ma, Parametric And Nonparametric Approaches For Multisensor Data Fusion, 2001.

E. Maggio and A. Cavallaro, Learning Scene Context for Multiple Object Tracking, IEEE Transactions on Image Processing, vol.18, issue.8, pp.1873-1884, 2009.
DOI : 10.1109/TIP.2009.2019934

E. Maggio, E. Piccardo, C. Regazzoni, and A. Cavallaro, Particle PHD Filtering for Multi-Target Visual Tracking, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007.
DOI : 10.1109/ICASSP.2007.366104

. Manohar, P. Vasant, H. Soundararajan, D. Raju, R. Goldgof et al., Performance Evaluation of Object Detection and Tracking in Video, Computer Vision --ACCV 2006: 7th Asian Conference on Computer Vision Proceedings, Part II, 2006.
DOI : 10.1007/11612704_16

I. Markovic and I. Petrovic, Bayesian Sensor Fusion Methods for Dynamic Object Tracking - A Comparative Study, Automatika ??? Journal for Control, Measurement, Electronics, Computing and Communications, vol.55, issue.4, pp.386-398, 2014.
DOI : 10.7305/automatika.2014.09.847

S. Mckeever and J. Ye, A Comparison of Evidence Fusion Rules for Situation Recognition in Sensor-Based Environments Evolving Ambient Intelligence: AmI 2013 Workshops Revised Selected Papers, pp.163-175978, 2013.

R. J. Meinhold, D. Nozer, and . Singpurwalla, Understanding the Kalman Filter, 1983.

M. Menze and A. Geiger, Object scene flow for autonomous vehicles, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3061-3070, 2015.
DOI : 10.1109/CVPR.2015.7298925
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.4185

J. Miller, Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture, 2008 IEEE Intelligent Vehicles Symposium, pp.715-720, 2008.
DOI : 10.1109/IVS.2008.4621301
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.1715

D. Molloy, Exploring Raspberry Pi: Interfacing to the Real World with Embedded Linux, 2016.
DOI : 10.1002/9781119211051

B. Mourllion, D. Gruyer, C. Royere, and S. Theroude, Multihypotheses tracking algorithm based on the belief theory, 7th International Conference on Information Fusion, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01086042

S. Mutlu, T. Hu, and O. Lanz, Learning the Scene Illumination for Color-Based People Tracking in Dynamic Environment, Image Analysis and Processing -- ICIAP 2013: 17th International Conference Proceedings , Part II, pp.683-692, 2013.
DOI : 10.1007/978-3-642-41184-7_69

A. Napier, P. Corke, and P. Newman, Cross-calibration of push-broom 2D LIDARs and cameras in natural scenes, 2013 IEEE International Conference on Robotics and Automation, pp.3679-3684, 2013.
DOI : 10.1109/ICRA.2013.6631094

V. Neagoe and C. Tudoran, Road following for autonomous vehicle navigation using a concurrent neural classifier''. In: 2008 World Automation Congress, pp.1-6, 2008.

K. Ng, E. J. Ki, and . Delp, New models for real-time tracking using particle filtering, Visual Communications and Image Processing 2009, 2009.
DOI : 10.1117/12.807311
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.585.3033

F. Ofli, R. Chaudhry, G. K. Vidal, and R. Bajcsy, Berkeley MHAD: A comprehensive Multimodal Human Action Database, 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp.53-60, 2013.
DOI : 10.1109/WACV.2013.6474999

E. Olson, A Primer on Odometry and Motor Control. url: http : / / ocw . mit . edu / courses / electrical -engineering -and -computer -science / 6 -186 mobile -autonomous -systems -laboratory -january -iap -2005 / study -materials, 2004.

U. Orguner, T. B. Schon, and F. Gustafsson, Improved target tracking with road network information, 2009 IEEE Aerospace conference, pp.1-11, 2009.
DOI : 10.1109/AERO.2009.4839490

G. Pandey, J. R. Mcbride, S. Savarese, and R. M. Eustice, Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information, Journal of Field Robotics, vol.22, issue.11, 2014.
DOI : 10.1002/rob.21542

H. S. Parekh, G. Darshak, U. K. Thakore, and . Jaliya, A Survey on Object Detection and Tracking Methods, International Journal of Innovative Research in Computer and Communication Engineering, vol.2, 2014.

L. Patino and J. Ferryman, PETS 2014: Dataset and challenge''. In: Advanced Video and Signal Based Surveillance (AVSS), 11th IEEE International Conference on, pp.355-360, 2014.
DOI : 10.1109/avss.2014.6918694

A. Petrovskaya and S. Thrun, Model based vehicle detection and tracking for autonomous urban driving, Autonomous Robots, vol.58, issue.2, pp.123-139, 2009.
DOI : 10.1007/s10514-009-9115-1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.2912

C. Pohl and J. L. Van-genderen, Review article Multisensor image fusion in remote sensing: Concepts, methods and applications, International Journal of Remote Sensing, vol.19, issue.5, pp.823-854, 1998.
DOI : 10.1080/014311698215748

S. Pramanik and D. Bhattacharjee, Multi-sensor image fusion based on moment calculation, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, pp.447-451, 2012.
DOI : 10.1109/PDGC.2012.6449862

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

C. Premebida and U. J. Nunes, Laser and Image Pedestrian Detection Dataset -LIPD. url: http://www2.isr.uc.pt, p.86, 2009.

C. Premebida, P. Peixoto, and U. Nunes, Tracking and Classification of Dynamic Obstacles Using Laser Range Finder and Vision, IEEERSJ Internation Conference on Intelligent Robots and Systems, p.35, 2006.

M. A. Quddus, Y. Washington, R. B. Ochieng, and . Noland, Current map-matching algorithms for transport applications: State-of-the art and future research directions''. In: Transportation Research Part C: Emerging Technologies 15, pp.312-328, 2007.

. Quigley, ROS: an open-source Robot Operating System, ICRA Workshop on Open Source Software, 2009.

S. Rezatofighi, A. Hamid, Z. Milan, Q. Zhang, A. Shi et al., Joint Probabilistic Data Association Revisited, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2015-3047, 2015.
DOI : 10.1109/ICCV.2015.349

S. Rodriguez, V. Alberto, P. Fremont, and . Bonnifait, Influence of Intrinsic Parameters over Extrinsic Calibration between a Multi-Layer Lidar and a Camera, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00464911

S. Rodriguez, V. Alberto, P. Fremont, V. Bonnifait, and . Cherfaoui, Multi-Modal Object Detection and Localization for High Integrity Driving Assistance, Machine Vision Applications 1, pp.1-18, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00777387

J. Sachs, M. Aftanas, J. S. Crabbe, M. Drutarovsky, R. Klukas et al., Detection and tracking of moving or trapped people hidden by obstacles using ultra-wideband pseudo-noise radar, Radar Conference, pp.408-411, 2008.

V. Salari and I. Sethi, Feature point correspondence in the presence of occlusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.1, pp.87-91, 1990.
DOI : 10.1109/34.41387

A. C. Sankaranarayanan, A. Veeraraghavan, and R. Chellappa, Object Detection, Tracking and Recognition for Multiple Smart Cameras, Proceedings of the IEEE 96, pp.1606-1624, 2008.
DOI : 10.1109/JPROC.2008.928758
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.7750

K. Sato and J. K. Aggarwal, Temporal spatio-velocity transform and its application to tracking and interaction, Computer Vision and Image Understanding, vol.96, issue.2, pp.100-128, 2004.
DOI : 10.1016/j.cviu.2004.02.003
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.633.5514

E. Sattarov, S. A. Rodr\-'\i-guez-florez, A. Gepperth, and R. Reynaud, Context-based vector fields for multi-object tracking in application to road traf- REFERENCES fic, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1179-1185, 2014.

E. Sattarov, A. Gepperth, S. A. , R. Florez, and R. Reynaud, Calibration-free match finding between vision and LIDAR, 2015 IEEE Intelligent Vehicles Symposium (IV), pp.2015-1061, 2015.
DOI : 10.1109/IVS.2015.7225825
URL : https://hal.archives-ouvertes.fr/hal-01292529

D. Scaramuzza, A. Harati, and R. Siegwart, Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4164-4169, 2007.
DOI : 10.1109/IROS.2007.4399276

G. Schall, D. Wagner, G. Reitmayr, E. Taichmann, M. Wieser et al., Global pose estimation using multisensor fusion for outdoor Augmented Reality, p.31, 2009.
DOI : 10.1109/ismar.2009.5336489
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.6860

. Scharw\-"-achter, M. Timo, U. Enzweiler, S. Franke, and . Roth, Efficient Multicue Scene Segmentation, Pattern Recognition: 35th German Conference, GCPR 2013, Saarbr\"ucken, pp.435-445978, 2013.

V. Part and . Cham, Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding, Computer Vision --ECCV 2014: 13th European Conference Proceedings, pp.533-548, 2014.

B. M. Scherzinger, Precise Robust Positioning with Inertial, GPS RTK, 2000.

M. Schikora, A. Gning, L. Mihaylova, D. Cremers, and W. Koch, Box-particle probability hypothesis density filtering, IEEE Transactions on Aerospace and Electronic Systems, vol.50, issue.3, pp.1660-1672, 2014.
DOI : 10.1109/TAES.2014.120238
URL : http://eprints.whiterose.ac.uk/82259/7/box-phdFilter_doubleColumn_submitted.pdf

G. Shafer, A Mathematical Theory of Evidence, 1976.

K. Sharma, K. Jeong, and S. Kim, Vision based autonomous vehicle navigation with self-organizing map feature matching technique, Control, Automation and Systems (ICCAS) 11th International Conference on, pp.946-949, 2011.

J. Shi and C. Tomasi, Good Features to Track, IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), pp.593-600, 1994.

N. Shibata, S. Sugiyama, and T. Wada, Collision avoidance control with steering using velocity potential field, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp.2014-438, 2014.
DOI : 10.1109/IVS.2014.6856469

S. Shimojo and L. Shams, Sensory modalities are not separate modalities: plasticity and interactions, Current Opinion in Neurobiology, vol.11, issue.4, 2001.
DOI : 10.1016/S0959-4388(00)00241-5

P. Smets, Belief functions versus probability functions, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.17-24, 1988.
DOI : 10.1007/3-540-19402-9_51

J. Wiley, \. Sons, . Inc, and . Chap, What is Dempster-Shafer's Model?, pp.5-34

K. Smith, D. Gatica-perez, J. Odobez, and S. Ba, Evaluating Multi-Object Tracking, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Workshops, pp.36-36, 2005.
DOI : 10.1109/CVPR.2005.453

C. Stiller, F. Puente-le\-'on, and M. Kruse, Information fusion for automotive applications ??? An overview, Information Fusion, vol.12, issue.4, pp.244-252, 2011.
DOI : 10.1016/j.inffus.2011.03.005

S. Strygulec, D. M\-"-uller, M. Meuter, C. Nunn, S. Ghosh et al., ohler (2013). ``Road Boundary Detection and Tracking using monochrome camera images

Z. Sun, G. Bebis, and R. Miller, On-road vehicle detection: a review, 2006.

S. Sunderrajan and B. S. Manjunath, Multiple view discriminative appearance modeling with IMCMC for distributed tracking, 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), 2013.
DOI : 10.1109/ICDSC.2013.6778203

T. Svoboda, H. Hug, and L. Van-gool, ViRoom ??? Low Cost Synchronized Multicamera System and Its Self-calibration, Pattern Recognition: 24th DAGM Symposium Zurich, Switzerland, 2002.
DOI : 10.1007/3-540-45783-6_62
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.9807

M. Taj, E. Maggio, and A. Cavallaro, Objective Evaluation of Pedestrian and Vehicle Tracking on the CLEAR Surveillance Dataset, p.REFERENCES, 2007.
DOI : 10.1007/978-3-540-68585-2_13

T. Takasu, RTKLIB ver. 2.4.2 Manual, 2013.

H. Tao, H. S. Sawhney, and R. Kumar, Object tracking with Bayesian estimation of dynamic layer representations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.241, pp.75-89, 2002.

. Gomez-skarmeta, High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS, IEEE Transactions on Intelligent Transportation Systems, vol.83, pp.491-511, 2007.

M. Ulmke and K. W. Koch, Road Map Extraction using GMTI Tracking, 2006 9th International Conference on Information Fusion, pp.1-7, 2006.
DOI : 10.1109/ICIF.2006.301564

C. Urmson, Autonomous Driving in Urban Environments: Boss and the Urban Challenge In: The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, pp.1-59, 2009.

M. Vasic and A. Martinoli, A Collaborative Sensor Fusion Algorithm for Multiobject Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp.491-498, 2015.
DOI : 10.1109/itsc.2015.87
URL : http://infoscience.epfl.ch/record/209199

A. Vatavu, A. D. Costea, and S. Nedevschi, Modeling and tracking of dynamic obstacles for logistic plants using omnidirectional stereo vision, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.3552-3558, 2015.
DOI : 10.1109/IROS.2015.7353873

A. Vatavu, R. Danescu, and S. Nedevschi, Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters, IEEE Transactions on Intelligent Transportation Systems 16.1, pp.498-511, 2015.
DOI : 10.1109/TITS.2014.2366248

C. J. Veenman, J. T. Marcel, E. Reinders, and . Backer, Resolving motion correspondence for densely moving points, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.1, pp.54-72, 2001.
DOI : 10.1109/34.899946

B. Vo and W. Ma, The Gaussian Mixture Probability Hypothesis Density Filter, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4091-4104, 2006.
DOI : 10.1109/TSP.2006.881190

. Vo, S. Ba-ngu, A. Singh, and . Doucet, Sequential monte carlo implementation of the phd filter for multi-target tracking''. In: Information Fusion, Proceedings of the Sixth International Conference of, pp.792-799, 2003.

. Vu, O. Trung-dung, F. Aycard, and . Tango, Object perception for intelligent vehicle applications: A multi-sensor fusion approach, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp.774-780, 2014.
DOI : 10.1109/IVS.2014.6856588
URL : https://hal.archives-ouvertes.fr/hal-01019527

M. Wang, Y. Dai, Y. Liu, and T. Yanbing, Feature-level image sequence fusion based on histograms of Oriented Gradients, 2010 3rd International Conference on Computer Science and Information Technology, pp.265-269, 2010.
DOI : 10.1109/ICCSIT.2010.5563759

Z. Wang, S. Li, Q. Cai, S. Su, and M. Liu, Multi-spectrum image fusion algorithm based on weighted and improved wavelet transform, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009.
DOI : 10.1109/ICICISYS.2009.5357741

G. Welch and E. Foxlin, Motion tracking: no silver bullet, but a respectable arsenal, IEEE Computer Graphics and Applications, vol.22, issue.6, pp.24-38, 2002.
DOI : 10.1109/MCG.2002.1046626

X. Wen, Image Fusion Based on Improved IHS Transform with Weighted Average, 2011 International Conference on Computational and Information Sciences, 2011.
DOI : 10.1109/ICCIS.2011.162

H. Wu, Sensor Data Fusion for Context-aware Computing Using Dempstershafer Theory, 2004.

Z. Wu, N. Fuller, D. Theriault, and M. Betke, A Thermal Infrared Video Benchmark for Visual Analysis, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.201-208, 2014.
DOI : 10.1109/CVPRW.2014.39

A. Yilmaz, O. Javed, and M. Shah, Object tracking, ACM Computing Surveys, vol.38, issue.4, 2006.
DOI : 10.1145/1177352.1177355

F. Yin, D. Makris, and S. A. Velastin, Performance evaluation of object tracking algorithms''. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, p.25, 2007.

Z. Zhang, Flexible camera calibration by viewing a plane from unknown orientations, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.666-673, 1999.
DOI : 10.1109/ICCV.1999.791289

S. Zheng, Pixel-level Image Fusion Algorithms for Multi-camera Imaging System, 2010.

Y. Zheng, Z. Shi, R. Lu, S. Hong, and X. Shen, An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking, IEEE Transactions on Industrial Informatics 9.4, pp.2318-2326, 2013.
DOI : 10.1109/TII.2012.2228875

. Synth\èse-substantiel-aujourd-'hui, ehicules intelligents sont des applications qui comportent de verrous scientifiques et technologiques importants Parmi ses verrous, nous portons un int\'er\^et particulier auxprobl\èmes li\'es \`a la localisation, l'analyse et la compr\'ehension desc\ène dynamique et aux interactions d'un v\'ehicule autonome avec d'autres v\'ehicules, avec l'infrastructure et avec les usagers vuln\

\. Probl\èmes and . Etudi\, etude des m\'ethodes de suivi multi-objets bas\'ees sur la perception embarqu\'ee est d'une grande importance. Le suivi d'objets dynamiques est unprobl\ème complexe d\^u principalement aux sp\'ecificit\'es et \`a la multiplicit\'e de contraintes des environnements observ\'es (rural, semi-urbain, urbain) De plus, le m\'ethodes doivent aussi s'adapter aux limitations des capteurs (impr\'ecision, fiabilit\'e)

. Le-suivi-multi, evitement d'obstacles, alors, il doit \^etre pr\'ecis, continu etint\ègre Cescrit\ères de qualit\'e sont impact\'es n\'egativement quand les objets sont d\'etect\'es partialement ou m\^emecompl\ètement occlus. Les techniques de fusion de l'\'etat de l'art combinent l'information des sources multiples afin d'\'elargir le champ de vision en utilisant des capteurs avec une port\'ee diff\'erente. D'autres strat\'egies de fusion diminuent les occlusions et les non d\'etections. Toutes ces m\'ethodes sont limit\'ees par des erreursintrins\èques des capteurs

. Lemod\èle-d-'objet-utilis\-'e-est-d\-'efini-et-formalis\-'e, Ensuite, un accent est r\'eserv\'e aux m\'ethodes de suivi probabilistes Dans ladeuxi\ème section, unsyst\ème pour le suivi des objets multiples est propos\'e. Ce derniersyst\ème est bas\'e sur les m\'ethodes existantes. Lesyst\ème est d\'evelopp\'e et implant\'e. Lesyst\ème de suivi d'objets se base sur un formalise bay\'esien dans une impl\'ementation de type Monte-Carlo. Cette repr\'esentation permet l'int\'egration de l'information contextuelle

. Titre, Etude et quantification de la contribution dessyst\èmes de perception multimodale assist\'es par des informations de contexte pour la d\'etection et le suivi d'objets dynamiques Mot-clefs: Fusion des donn\'ees, perception, suivi, multimodalit\'e R\'esum\'e: Cetteth\èse a pour but d