. Aperçu and . .. De-parallélisme, , p.54

. .. Résultats,

. .. Conclusion, 77 charge de CS-MoG sur des plateformes hétérogènes CPU-GPU

, 100 5.2 Défis de l'équilibrage des charges de calcul

. .. Curseur-de-distribution-optimale-de-données, 103 5.3.1 Cas critique : dominance du temps de transfert, p.108

. .. , Stabilisation de répartition : filtrage et quantification, p.110

. .. Résultats, 111 5.5.1 Exécution sur flux d'images

.. .. Conclusion,

L. A. Manwaring, The Observer's Book of Automobiles, 1966.

, Cars produced in the world -Worldometers, août 2019

P. R. Center, Global Attitudes Survey: U.S.-German relations, mars 2019, Methodology, 2018.

R. Saussard, TEL -Thèses en ligne -Méthodologies et outils de portage d'algorithmes de traitement d'images sur cibles hardware mixte, 2017.

C. Wong, E. Yang, X. Yan, and D. Gu, Adaptive and intelligent navigation of autonomous planetary rovers -A survey, NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp.237-244, 2017.

Y. Shen, W. Hu, M. Yang, J. Liu, B. Wei et al., « Real-Time and Robust Compressive Background Subtraction for Embedded Camera Networks », IEEE Transactions on Mobile Computing, t, vol.15, issue.2, pp.406-418, 2016.

H. Liu, T. Hong, M. Herman, T. Camus, and R. Chellappa, Accuracy vs Efficiency Trade-offs in Optical Flow Algorithms », Computer Vision and Image Understanding, pp.271-286, 1998.

S. Saha, S. Basu, M. Nasipuri, and D. K. Basu, « An Offline Technique for Localization of License Plates for Indian Commercial Vehicles, 2010.

C. Creusot and A. Munawar, Real-time small obstacle detection on highways using compressive RBM road reconstruction, pp.162-167, 2015.

O. Bourja, K. Kabbaj, H. Derrouz, A. E. Bouziady, R. O. Thami et al., Moroccan Video Intelligent Transport System », IEEE 5th International Congress on Information Science and Technology (CiSt), pp.502-507, 2018.

S. Sivaraman and M. M. Trivedi, « Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis, IEEE Transactions on Intelligent Transportation Systems, t, vol.14, issue.4, pp.1773-1795, 2013.

M. Piccardi, IEEE International Conference on Systems, Man and Cybernetics, vol.4, pp.3099-3104, 2004.

C. Stauffer and W. E. Grimson, « Adaptive background mixture models for real-time tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, pp.246-252, 1999.

M. Heredia-conde, Fundamentals of Compressive Sensing », Compressive Sensing for the Photonic Mixer Device: Fundamentals, Methods and Results, M. Heredia Conde, pp.89-205, 2017.

A. and J. Shameem,

. Roberts, Multi-Core Programming Increasing Performance through Software Multithreading, 2006.

L. Mabrouk, D. Houzet, S. Huet, S. Belkouch, A. Hamzaoui et al., Single Core SIMD Parallelization of GMM Background Subtraction Algorithm for Vehicles Detection », IEEE 5th International Congress on Information Science and Technology (CiSt), pp.308-312, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01922393

J. Reinders, Intel® AVX-512 Instructions, 2013.

H. Persyval-team and . Architectures, Versatile Exploitation and programing | Persyval-Lab, 2019.

L. Persyval, |. Persyval-lab, and . Persyval-lab, , 2019.

P. Druzhkov and K. Valentina, « A survey of deep learning methods and software tools for image classification and object detection », Pattern Recognition and Image Analysis, t. 26, pp.9-15, 2016.

V. Mondéjar-guerra, J. Rouco, J. Novo, and M. Ortega, « An end-to-end deep learning approach for simultaneous background modeling and subtraction, British Machine Vision Conference (BMVC), 2019.

Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, « Review and evaluation of commonly-implemented background subtraction algorithms », 19th International Conference on Pattern Recognition, pp.1-4, 2008.

T. Bouwmans and . Subspace, Learning for Background Modeling: A Survey, pp.223-234, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00534555

T. Bouwmans, « Recent Advanced Statistical Background Modeling for Foreground Detection -A Systematic Survey, pp.147-176, 2011.

D. Stalin-alex, A. Wahi, and «. Bsfd, Background subtraction frame difference algorithm for moving object detection and extraction, Journal of Theoretical and Applied Information Technology, t, vol.60, pp.623-628, 2014.

B. Lee and M. Hedley, « Background estimation for video surveillance », Image and amp, Vision Computing New Zealand (IVCNZ '02), 2002.

B. Laugraud, S. Piérard, M. Braham, M. Van-droogenbroeck, ;. V. Murino et al., « Simple Median-Based Method for Stationary Background Generation Using Background Subtraction Algorithms, New Trends in Image Analysis and Processing -ICIAP 2015 Workshops

M. Sona, C. Cristani, and . Sansone, , pp.477-484, 2015.

N. J. Mcfarlane and C. P. Schofield, « Segmentation and tracking of piglets in images, Machine Vision and Applications, pp.187-193, 1995.

J. Zheng, Y. Wang, N. L. Nihan, and M. E. Hallenbeck, Extracting roadway background image: Mode-based approach », Transportation research record, pp.82-88, 1944.

E. J. Candès, X. Li, Y. Ma, and J. Wright, « Robust Principal Component Analysis?, J. ACM, t, vol.58, p.37, 2011.

K. Toyama, J. Krumm, B. Brumitt, B. Meyers, and . Wallflower, Principles and practice of background maintenance, Proceedings of the seventh IEEE, pp.255-261, 1999.

S. Messelodi, C. M. Modena, N. Segata, and M. Zanin, « A kalman filter based background updating algorithm robust to sharp illumination changes, International Conference on Image Analysis and Processing, pp.163-170, 2005.

R. Chang, T. Gandhi, and M. M. Trivedi, « Vision modules for a multi-sensory bridge monitoring approach, The 7th International IEEE Conference on Intelligent Transportation Systems, pp.971-976, 2004.

M. H. Sigari, N. Mozayani, and H. Pourreza, « Fuzzy running average and fuzzy background subtraction: concepts and application, International Journal of Computer Science and Network Security, issue.2, pp.138-143, 2008.

F. E. Baf, T. Bouwmans, and B. Vachon, « Type-2 fuzzy mixture of Gaussians model: application to background modeling, International Symposium on Visual Computing, pp.772-781, 2008.

H. Zhang and D. Xu, « Fusing color and texture features for background model », Fuzzy Systems and Knowledge Discovery: Third International Conference, vol.3, pp.887-893, 2006.

F. E. Baf, T. Bouwmans, and B. Vachon, « Fuzzy integral for moving object detection, IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), pp.1729-1736, 2008.

D. Manjula and M. Sivabalakrishnan, Adaptive background subtraction in dynamic environments using fuzzy logic », Int. J. Video Image Process. Netw. Secur, t, vol.10, pp.13-16, 2010.

D. Culibrk, O. Marques, D. Socek, H. Kalva, and B. Furht, Neural Network Approach to Background Modeling for Video Object Segmentation », IEEE Transactions on Neural Networks, t, vol.18, pp.1614-1627, 2007.

S. Biswas, J. Sil, and N. Sengupta, « Background modeling and implementation using discrete wavelet transform: a review », Journal ICGST-GVIP, issue.11, pp.29-42, 2011.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, « Background modeling and subtraction by codebook construction, 2004 International Conference on Image Processing, vol.5, pp.3061-3064, 2004.

O. Barnich, M. Droogenbroeck, and . Vibe, A Universal Background Subtraction Algorithm for Video Sequences », Image Processing, IEEE Transactions, vol.20, pp.1709-1724, 2011.

M. Dikmen and T. S. Huang, « Robust estimation of foreground in surveillance videos by sparse error estimation, 19th International Conference on Pattern Recognition, pp.1-4, 2008.

J. Lee and M. Park, « An Adaptive Background Subtraction Method Based on Kernel Density Estimation, Sensors, pp.12-279

D. Butler, S. Sridharan, and V. J. Bove, « Real-time adaptive background segmentation », IEEE International Conference on Acoustics, Speech, and Signal Processing, p.349, 2003.

C. R. Wren, A. Azarbayejani, T. Darrell, A. P. Pentland, and . Pfinder, real-time tracking of the human body », IEEE Transactions on Pattern Analysis and Machine Intelligence, t. 19, pp.780-785, 1997.

N. Friedman and S. Russell, « Image Segmentation in Video Sequences: A Probabilistic Approach », Proc. 13th Conf. on Uncertainty in Artificial Intelligence, fév, 2013.

. Hayman and . Eklundh, Proceedings Ninth IEEE International Conference on Computer Vision, vol.1, pp.67-74, 2003.

H. Cramér, Random variables and probability distributions, p.36, 2004.

A. Sobral and A. Vacavant, « A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos », Computer Vision and Image Understanding, pp.4-21, 2014.

G. Szwoch, D. Ellwart, and A. Czy?ewski, « Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform, Journal of Real-Time Image Processing, pp.111-125, 2016.

T. K. Moon, « The expectation-maximization algorithm, pp.47-60, 1996.

Z. Zivkovic and F. Van-der-heijden, Efficient adaptive density estimation per image pixel for the task of background subtraction », Pattern Recognition Letters, t, vol.27, pp.773-780, 2006.

J. Cheng, J. Yang, Y. Zhou, and Y. Cui, « Flexible background mixture models for foreground segmentation, Image and Vision Computing, t, vol.24, issue.5, pp.473-482, 2006.

D. Lee, « Online adaptive Gaussian mixture learning for video applications, ternational Workshop on Statistical Methods in Video Processing, pp.105-116, 2004.

M. Amintoosi, F. Farbiz, M. Fathy, M. Analoui, and N. Mozayani, « QR decomposition-based algorithm for background subtraction, IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, p.1093, 2007.

Q. Zang and R. Klette, « Evaluation of an adaptive composite Gaussian model in video surveillance, International Conference on Computer Analysis of Images and Patterns, pp.165-172, 2003.

J. Lindstrom, F. Lindgren, K. Ltrstrom, and J. , Holst et U. Holst, « Background and foreground modeling using an online EM algorithm », The sixth IEEE international workshop on visual surveillance, pp.9-16, 2006.

Y. Ren, C. Chua, and Y. Ho, « Motion detection with nonstationary background », Machine Vision and Applications, t, vol.13, pp.332-343, 2003.

D. Lee, « Improved Adaptive Mixture Learning for Robust Video Background Modeling. », MVA-Workshop on Machine Vision Applications, pp.443-446, 2002.

A. Pnevmatikakis and L. Polymenakos, « 2D person tracking using Kalman filtering and adaptive background learning in a feedback loop », International Evaluation Workshop on Classification of Events, Activities and Relationships, pp.151-160, 2006.

S. Yang and C. Hsu, « Background modeling from GMM likelihood combined with spatial and color coherency, 2006 International Conference on Image Processing, pp.2801-2804, 2006.

J. L. Landabaso and M. Pardas, « Cooperative background modelling using multiple cameras towards human detection in smart-rooms, 14th European Signal Processing Conference, pp.1-5, 2006.

M. Haque, M. Murshed, and M. Paul, « A hybrid object detection technique from dynamic background using Gaussian mixture models, IEEE 10th Workshop on Multimedia Signal Processing, pp.915-920, 2008.

X. Fang, W. Xiong, B. Hu, and L. Wang, « A moving object detection algorithm based on color information, Journal of Physics: Conference Series, t. 48, p.384, 2006.

H. Bhaskar, L. Mihaylova, and S. Maskell, Automatic target detection based on background modeling using adaptive cluster density estimation, Gesellschaft für Informatik, 2007.

M. Xu and T. Ellis, « Illumination-Invariant Motion Detection Using Colour Mixture Models, British Machine Vision Conference (BMVC), pp.1-10, 2001.

W. Wang and R. Wu, Fusion of luma and chroma GMMs for HMM-based object detection », Pacific-Rim Symposium on Image and Video Technology, pp.573-581, 2006.

O. Javed, K. Shafique, and M. Shah, « A hierarchical approach to robust background subtraction using color and gradient information, Workshop on Motion and Video Computing, pp.22-27, 2002.

Y. Tian and A. Hampapur, « Robust salient motion detection with complex background for real-time video surveillance, Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05, vol.1, pp.30-35, 2005.

G. Gordon, T. Darrell, M. Harville, and J. Woodfill, « Background estimation and removal based on range and color, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp.459-464, 1999.

P. Dickinson and A. Hunter, « Scene modelling using an adaptive mixture of Gaussians in colour and space, pp.64-69, 2005.

H. Zheng, Z. Liu, and X. Wang, « Research on the video segmentation method with integrated multi-features based on GMM, International Conference on Computational Intelligence for Modelling Control and Automation, pp.260-264, 2008.

Z. Zhang, A. J. Lipton, P. L. Venetianer, and W. Yin, Background modeling with feature blocks. US Patent US8150103B2, avr, 2009.

Y. Tian, M. Lu, and A. Hampapur, « Robust and efficient foreground analysis for real-time video surveillance, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05, pp.1182-1187, 2005.

A. Mittal, N. Paragios, V. Ramesh, and A. Monnet, Method for scene modeling and change detection. US Patent US7336803B2, fév, 2008.

L. Meng, W. Wu, and Y. Li, System and method for segmenting foreground and background in a video. US Patent US8280165B2, 2012.

P. Kumar and K. Sengupta, « Foreground background segmentation using temporal and spatial markov processes, 2000.

Y. Sun and B. Yuan, « Hierarchical GMM to handle sharp changes in moving object detection, Electronics Letters, t, vol.40, pp.801-802, 2004.

Q. Zang and R. Klette, Proceedings of the 17th International Conference on Pattern Recognition, pp.90-93, 2004.

T. Su and J. Hu, « Background removal in vision servo system using Gaussian mixture model framework, IEEE International Conference on Networking, Sensing and Control, pp.70-75, 2004.

D. H. Parks and S. S. Fels, « Evaluation of background subtraction algorithms with post-processing », IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp.192-199, 2008.

J. Zhang and C. H. Chen, « Moving objects detection and segmentation in dynamic video backgrounds, 2007 IEEE Conference on Technologies for Homeland Security, pp.64-69, 2007.

D. Zhou and H. Zhang, « Modified GMM background modeling and optical flow for detection of moving objects, IEEE international conference on systems, man and cybernetics, pp.2224-2229, 2005.

P. Jaikumar, A. Singh, and S. K. Mitra, Background Subtraction in Videos using Bayesian Learning with Motion Information. », British Machine Vision Conference (BMVC), t, pp.615-639, 2008.

A. Shimada and R. Taniguchi, « Object detection based on Gaussian mixture predictive background model under varying illumination, 2008.

C. Lien, Y. Jiang, and L. Jang, « Large Area Video Surveillance System with Handoff Scheme among Multiple Cameras. », MVA-Workshop on Machine Vision Applications, pp.463-466, 2009.

M. Najjar, S. Ghosh, and M. Bayoumi, « A hybrid adaptive scheme based on selective Gaussian modeling for real-time object detection, IEEE International Symposium on Circuits and Systems, pp.936-939, 2009.

S. Xuehua, C. Yu, G. Jianfeng, and C. Jingzhu, « A robust moving objects detection algorithm based on Gaussian mixture model, 2009 International Conference on Information Technology and Computer Science, t. 1, pp.566-569, 2009.

R. Yan, X. Song, and S. Yan, « Moving object detection based on an improved Gaussian mixture background model, ISECS International Colloquium on Computing, Communication, Control, and Management, pp.12-15, 2009.

D. Xie, « Background model initializing and updating method based on video monitoring », China Patent CN101489121B, 2009.

P. K. Atrey, V. Kumar, A. Kumar, and M. S. Kankanhalli, « Experiential sampling based foreground/background segmentation for video surveillance, 2006.

, IEEE International Conference on Multimedia and Expo, pp.1809-1812, 2006.

D. R. Magee, « Tracking multiple vehicles using foreground, background and motion models », Image and vision Computing, t. 22, pp.143-155, 2004.

R. Krishna, K. Mccusker, and N. E. O'connor, Optimising resource allocation for background modeling using algorithm switching, Second ACM/IEEE International Conference on Distributed Smart Cameras, pp.1-7, 2008.

Y. Liang, Z. Wang, X. Xu, and X. Cao, « Background Pixel Clissification for Motion Segmentation Using Mean Shift Algorithm, 2007 International Conference on Machine Learning and Cybernetics, pp.1693-1698, 2007.

H. Jiang, H. Ardo, and V. Owall, « Hardware accelerator design for video segmentation with multi-modal background modelling, IEEE International Symposium on Circuits and Systems, pp.1142-1145, 2005.

K. Appiah and A. Hunter, « A single-chip FPGA implementation of real-time adaptive background model, Proceedings. 2005 IEEE International Conference on Field-Programmable Technology, pp.95-102, 2005.

J. Yang, X. Yuan, X. Liao, P. Llull, D. J. Brady et al., Video Compressive Sensing Using Gaussian Mixture Models », IEEE Transactions on Image Processing, pp.4863-4878, 2014.

A. Kulkarni and T. Mohsenin, « Accelerating compressive sensing reconstruction OMP algorithm with CPU, GPU, FPGA and domain specific many-core, 2015.

, IEEE International Symposium on Circuits and Systems (ISCAS), pp.970-973, 2015.

M. Petrovici, C. Damian, C. Udrea, F. Garoi, and D. Coltuc, Single Pixel Camera with Compressive Sensing by non-uniform sampling, 2016 International Conference on Communications (COMM), pp.443-448, 2016.

M. Liang, Y. Li, M. A. Neifeld, and H. Xin, « Principal Component Analysis (PCA) based compressive sensing millimeter wave imaging system, USNC-URSI Radio Science Meeting, pp.341-341, 2015.

N. Ponomarenko, V. Lukin, K. Egiazarian, and J. Astola, « DCT based high quality image compression, Scandinavian Conference on Image Analysis, pp.1177-1185, 2005.

P. G. Center, Pixel-wise Image Processing, 2016.

P. Rogers, Heterogeneous system architecture overview », 2013 IEEE Hot Chips 25 Symposium (HCS), pp.1-41, 2013.

Y. Yang, P. Xiang, M. Mantor, and H. Zhou, « CPU-assisted GPGPU on fused CPU-GPU architectures, IEEE International Symposium on High-Performance Comp Architecture, pp.1-12

S. Azmat, L. Wills, and S. Wills, « Accelerating Adaptive Background Modeling on Low-Power Integrated GPUs », 2012 41st International Conference on Parallel Processing Workshops, pp.568-573, 2012.

, TOP500, TOP 10 Sites for, 2019.

M. Mindfactory and . Report, , 2019.

. Intel, Processeurs Intel® pour serveurs, PC, IoT et appareils mobiles

. Wikipedia, List of Nvidia graphics processing units

C. Microway, N. Between, T. Geforce, and . Gpus,

S. Markidis, S. W. Der-chien, E. Laure, I. B. Peng, and J. S. Vetter, « Nvidia tensor core programmability, performance & precision », IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.522-531, 2018.

J. Sanders and E. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, Portable Documents, 1 edition. Addison-Wesley Professional, juill, 2010.

V. Boulos, Adéquation Algorithme Architecture et modèle de programmation pour l'implémentation d'algorithmes de traitement du signal et de l'image sur cluster multi-GPU », thèse de doct., déc, 2012.

S. Wienke, P. Springer, C. Terboven, and D. A. Mey, Euro-Par 2012 Parallel Processing, pp.859-870

J. E. Stone, D. Gohara, G. Shi, and . Opencl, A Parallel Programming Standard for Heterogeneous Computing Systems, Computing in Science Engineering, t, vol.12, issue.3, pp.66-73, 2010.

R. Membarth, F. Hannig, J. Teich, M. Körner, and W. Eckert, « Frameworks for GPU accelerators: A comprehensive evaluation using 2D/3D image registration, 2011 IEEE 9th Symposium on Application Specific Processors (SASP), pp.78-81, 2011.

A. Williams and V. B. Escriba, The Boost Thread library, 2011.

A. Kukanov and M. J. Voss, The Foundations for Scalable Multi-core Software in Intel Threading Building Blocks. », Intel Technology Journal, 2007.

B. Chapman, G. Jost, and R. V. Pas, Using OpenMP: Portable Shared Memory Parallel Programming, 2007.

S. Chellappa, F. Franchetti, and M. Püschel, « How to write fast numerical code: A small introduction, International Summer School on Generative and Transformational Techniques in Software Engineering, pp.196-259, 2007.

J. L. Bentley, Writing efficient programs, 1982.

P. Hsieh, « Programming optimization, Retrieved from Azillion Monkeys, 2016.

. Gnu-project, GCC 5 Release Series -Changes, New Features, and Fixes

X. Tian, A. Bik, M. Girkar, P. Grey, H. Saito et al., Intel® OpenMP C++/Fortran Compiler for Hyper-Threading Technology: Implementation and Performance. », Intel Technology Journal, 2002.

P. Kaewtrakulpong and R. Bowden, « An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection, p.2

, European Workshop on Advanced Video-Based Surveillance Systems, 2001.

L. Mabrouk, S. Huet, D. Houzet, S. Belkouch, A. Hamzaoui et al., « Efficient parallelization of GMM background subtraction algorithm on a multicore platform for moving objects detection », 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp.1-5, 2018.

S. , Lee et C.-s. Jeong, « Real-time Object Segmentation based on GPU, 2006.

P. Carr and «. Gpu, Accelerated Multimodal Background Subtraction, Digital Image Computing: Techniques and Applications, pp.279-286, 2008.

P. Kumar, A. Singhal, S. Mehta, and A. , « Real-time moving object detection algorithm on high-resolution videos using GPUs, Journal of Real-Time Image Processing, pp.93-109, 2016.

V. Pham, P. Vo, and V. T. Hung, « GPU implementation of extended gaussian mixture model for background subtraction », IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp.1-4, 2010.

Y. Li, G. Wang, and X. Lin, « Three-level GPU accelerated Gaussian mixture model for background subtraction », Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, vol.8295, p.829, 2012.

P. Guler, D. Emeksiz, A. Temizel, and M. , Teke et T. Taskaya Temizel, « Realtime multi-camera video analytics system on GPU, Journal of Real-Time Image Processing, 2013.

C. Chen, N. Zhang, S. Shi, and D. Mu, « An efficient method for incremental learning of GMM using CUDA, Computer Science and Service System, pp.2141-2144, 2012.

B. Liu, W. Qiu, L. Jiang, and Z. Gong, « Software pipelining for graphic processing unit acceleration: Partition, scheduling and granularity », The International Journal of High Performance Computing Applications, t. 30, pp.169-185, 2016.

Y. Liang, M. T. Satria, K. Rupnow, and D. Chen, « An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization, Trans. Comp.-Aided Des. Integ. Cir. Sys., t, vol.35, issue.7, pp.1165-1178, 2016.

P. Janus and T. Kryjak, « Hardware implementation of the Gaussian Mixture Model foreground object segmentation algorithm working with ultra-high resolution video stream in real-time, Signal Processing -Algorithms, Architectures, Arrangements, and Applications (SPA), pp.140-145, 2018.

C. Zhang, H. Tabkhi, and G. Schirner, « A GPU-Based Algorithm-Specific Optimization for High-Performance Background Subtraction, 43rd International Conference on Parallel Processing, pp.182-191, 2014.

X. Ye and W. Wan, « Fast background modeling using GMM on GPU, 2014.

, International Conference on Audio, Language and Image Processing, pp.937-941, 2014.

A. Nurhadiyatna, R. Wijayanti, D. J. Fryantoni-;-k, N. Kim, and . Joukov, « Extended Gaussian Mixture Model Enhanced by Hole Filling Algorithm (GMMHF) Utilize GPU Acceleration », Information Science and Applications (ICISA) 2016, Lecture Notes in Electrical Engineering, pp.459-469, 2016.

P. Kova?ev, M. Mi?i?, and M. Toma?evi?, « Parallelization of the Mixture of Gaussians Model for Motion Detection on the GPU », Zooming Innovation in Consumer Technologies Conference (ZINC), pp.58-61, 2018.

R. K. and N. N. Chiplunkar, « A survey on techniques for cooperative CPU-GPU computing », Sustainable Computing: Informatics and Systems, t. 19, pp.72-85, 2018.

S. Mittal and J. S. Vetter, « A survey of CPU-GPU heterogeneous computing techniques, ACM Computing Surveys (CSUR), t. 47, p.69, 2015.

E. Stafford, B. Pérez, J. L. Bosque, R. Beivide, M. F. Valero-;-f et al., « To Distribute or Not to Distribute: The Question of Load Balancing for Performance or Energy, Parallel Processing, pp.710-722, 2017.

F. Zhang, J. Zhai, B. He, S. Zhang, and W. Chen, « Understanding Co-Running Behaviors on Integrated CPU/GPU Architectures », IEEE Transactions on Parallel and Distributed Systems, t. 28, pp.905-918, 2017.

D. Grewe, Z. Wang, and M. F. O'boyle, OpenCL Task Partitioning in the Presence of GPU Contention », Languages and Compilers for Parallel Computing, pp.87-101, 2014.

J. Shen, M. Arntzen, A. Varbanescu, and H. , Sips et D. Simons, « A framework for accelerating imbalanced applications on heterogeneous platforms », Computing Frontiers, pp.14-16, 2013.

A. K. Singh, A. Prakash, K. R. Basireddy, G. V. Merrett, and B. M. Al-hashimi, Energy-Efficient Run-Time Mapping and Thread Partitioning of Concurrent OpenCL Applications on CPU-GPU MPSoCs, vol.16, pp.1-147, 2017.

G. Teodoro, T. Pan, T. M. Kurc, J. Kong, L. A. Cooper et al., Efficient irregular wavefront propagation algorithms on hybrid CPU-GPU machines », Parallel computing, pp.189-211, 2013.

A. Navarro, F. Corbera, A. Rodriguez, A. Vilches, and R. Asenjo, « Heterogeneous parallel_for Template for CPU-GPU Chips, International Journal of Parallel Programming, 2018.

A. Vilches, R. Asenjo, A. Navarro, F. Corbera, R. Gran et al., Adaptive Partitioning for Irregular Applications on Heterogeneous CPU-GPU Chips », Procedia Computer Science, International Conference On Computational Science, ICCS 2015, t. 51, pp.140-149, 2015.

H. Li, T. Liang, and Y. Lin, « An OpenMP programming toolkit for hybrid CPU/GPU clusters based on software unified memory, Journal of Information Science and Engineering, t, vol.32, pp.517-539, 2016.

L. Li, R. Geda, A. B. Hayes, Y. Chen, P. Chaudhari et al.,

«. Szegedy, Simple Yet Effective Balanced Edge Partition Model for Parallel Computing, Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS '17 Abstracts, pp.6-6, 2017.

C. Augonnet, S. Thibault, R. Namyst, P. Wacrenier, and . Starpu, A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures, Parallel Processing, pp.863-874, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00384363

. Intel, Intel Intrinsics Guide

S. Blair-chappell, Intel Compiler Labs: The significance of SIMD, SSE and AVX

G. M. , Amdahl was at International Business Machines Corporation, Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities, Reprinted from the AFIPS Conference Proceedings, vol.30, pp.19-20, 1967.

L. Mabrouk, S. Huet, S. Belkouch, D. Houzet, Y. Zennayi et al., « Performance and Scalability Improvement of GMM Background Segmentation Algorithm on Multi-core Parallel Platforms, Proceedings of the 1st International Conference on Electronic Engineering and Renewable Energy, pp.120-127, 2018.

L. Mabrouk, S. Huet, D. Houzet, S. Belkouch, A. Hamzaoui et al., « Efficient adaptive load balancing approach for compressive background subtraction algorithm on heterogeneous CPU-GPU platforms, Journal of Real-Time Image Processing, 2019.

Y. Wang, P. Jodoin, F. Porikli, J. Konrad, Y. Benezeth et al., Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.387-394, 2014.

D. M. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, J. Mach. Learn. Technol, pp.2229-3981, 2011.

N. D. Blog, . Gpu-pro, and . Tip, CUDA 7 Streams Simplify Concurrency, 2015.

. Le-but-de-ce-projet-nommé-movits, Moroccan Video Intelligent Transport System), est de développer un système intégré de gestion de trafic et de détection d'infractions de circulations routière

, La fonction de cette couche est d'assurer l'acquisition des images à partir d'un système stéréoscopique calibré et synchronisé à l'aide d'une API Genicam. Ces images passent par une phase de prétraitement qui permet de changer l'espace de couleur, d'appliquer des filtres pour éliminer les bruits etc

, A.2.2 Extraction et analyse d'attributs

, Sur la base des images obtenues, cette couche est utilisée pour extraire les attributs statiques et dynamiques des véhicules nécessaires pour gérer le trafic : la détection des véhicules, extraction des trajectoires, les identifiés (plaque d'immatriculation, logo, couleur etc, extraction des éléments des signalisations verticales et horizontales dans la scène

, Dans cette couche s'effectue l'analyse des attributs

A. , 3 Activité illégale, analyse des flux de trafic et détection d'anomalies

, Sur la base de sorties des deux couches précédentes, cette couche fournit des services ITS pour la gestion efficace et le contrôle de la route

, Elle permet de détecter les infractions routières (tel que non-respect du stop/ feu rouge, excès de vitesse, fausse plaque d'immatriculation, changement de direction non autorisé

, Ensuite une phase d'analyse du flux trafic est mise en place pour gérer le trafic localement et globalement

A. Figure, 16 -Architecture modulaire des trois couches algorithmiques du projet Movits, vol.10

, Chacune de ces trois couches est composée de plusieurs modules, La Fig. A, vol.16

, Pipelining En informatique, le pipelining d'instruction est une technique pour implémenter le parallélisme au niveau de l'instruction dans un seul processeur. Le pipeline tente de garder chaque partie du processeur occupée par certaines instructions en divisant les instructions entrantes en une série d'étapes séquentielles (le "pipeline" éponyme) exécutées par différentes unités de processeur avec différentes parties d'instructions traitées en parallèle

, IF (Instruction Fetch) : Récupération de l'instruction

, ID (Instruction decode) : Décodage de l'instruction

, EX (Execution) : Exécution

, Accès mémoire

, La prédiction de branche est une caractéristique d'un processeur qui lui permet de prédire le résultat d'une branche, Cette technique permet à un processeur de rendre l'uti