Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories, 2011 International Conference on Computer Vision, pp.1419-1426, 2011. ,
DOI : 10.1109/ICCV.2011.6126397
Statistic and knowledge-based moving object detection in traffic scenes, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493), pp.27-32, 2000. ,
DOI : 10.1109/ITSC.2000.881013
URL : http://imagelab.ing.unimore.it/pubblicazioni/pubblicazioni/itsc2000.pdf
A survey on industrial vision systems, applications and tools, Image and Vision Computing, vol.21, issue.2, pp.171-188, 2003. ,
DOI : 10.1016/S0262-8856(02)00152-X
A Survey on Visual Surveillance of Object Motion and Behaviors, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.34, issue.3, pp.334-352, 2004. ,
DOI : 10.1109/TSMCC.2004.829274
Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems, IEEE Transactions on Consumer Electronics, vol.57, issue.3, pp.1165-1170, 2011. ,
DOI : 10.1109/TCE.2011.6018870
Design of video surveillance and tracking system based on attitude and heading reference system and PTZ camera, AIP Conference Proceedings 040016?, 2017. ,
DOI : 10.1063/1.4981612
Real-Time Object Tracking on a Drone With Multi-Inertial Sensing Data, IEEE Transactions on Intelligent Transportation Systems, vol.19, issue.1, pp.131-139, 2018. ,
DOI : 10.1109/TITS.2017.2750091
Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots, The International Journal of Robotics Research, vol.36, pp.13-14, 2017. ,
Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp.2840-2849, 2017. ,
DOI : 10.1109/ICCVW.2017.335
Tracking the trackers: an analysis of the state of the art in multiple object tracking, arXiv preprint, 2017. ,
Traditional and recent approaches in background modelling for foreground detection: An overview, Computer Science Review, issue.11, pp.31-66, 2014. ,
Object tracking, ACM Computing Surveys, vol.38, issue.4, p.13, 2006. ,
DOI : 10.1145/1177352.1177355
Pfinder: real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.780-785, 1997. ,
DOI : 10.1109/34.598236
Active contours approach to object tracking in image sequences with complex background, Pattern Recognition Letters, vol.16, issue.2, pp.171-178, 1995. ,
DOI : 10.1016/0167-8655(94)00086-I
URL : https://hal.archives-ouvertes.fr/hal-01441619
Object Segmentation by Long Term Analysis of Point Trajectories, pp.282-295, 2010. ,
DOI : 10.1007/978-3-642-15555-0_21
Background subtraction for moving camera based on trajectory-controlled segmentation and label inference, KSII Trans. on Internet and Information Systems, vol.9, issue.10, pp.4092-4107, 2015. ,
Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, pp.122-144, 2014. ,
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.3, pp.597-610, 2013. ,
DOI : 10.1109/TPAMI.2012.132
Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset, Computer Science Review, vol.23, pp.23-24, 2017. ,
DOI : 10.1016/j.cosrev.2016.11.001
URL : https://hal.archives-ouvertes.fr/hal-01522823
Handbook on Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing, 2016. ,
DOI : 10.1201/b20190
URL : https://hal.archives-ouvertes.fr/hal-01373013
OR-PCA with dynamic feature selection for robust background subtraction, Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC '15, pp.86-91, 2015. ,
DOI : 10.1007/978-3-642-37410-4_25
URL : https://hal.archives-ouvertes.fr/hal-01374214
Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.1-6, 2015. ,
DOI : 10.1109/AVSS.2015.7301753
URL : https://hal.archives-ouvertes.fr/hal-01227956
Efficient background subtraction with low-rank and sparse matrix decomposition, 2015 IEEE International Conference on Image Processing (ICIP), pp.4863-4867, 2015. ,
DOI : 10.1109/ICIP.2015.7351731
Translational and rotational jitter invariant incremental principal component pursuit for video background modelling, IEEE International Conference on Image Processing, pp.537-541, 2015. ,
DOI : 10.1109/icip.2015.7350856
Robust Pan, Tilt and Zoom Estimation for PTZ Camera by Using Meta Data and/or Frame-to-Frame Correspondences, 2006 9th International Conference on Control, Automation, Robotics and Vision, pp.1-7, 2006. ,
DOI : 10.1109/ICARCV.2006.345423
Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction, IEEE Transactions on Circuits and Systems for Video Technology, vol.27, issue.2, pp.236-248, 2017. ,
DOI : 10.1109/TCSVT.2015.2493499
A survey on moving object detection and tracking in video surveillance system, International Journal of Soft Computing and Engineering (IJSCE), vol.2, issue.3, pp.2231-2307, 2012. ,
A survey and comparative analysis of moving object detection and tracking, International Journal of Engineering Research & Technology (IJERT), vol.2, issue.10, pp.3616-3621, 2013. ,
Recent advanced statistical background modelling for foreground detection: A systematic survey, In Recent Patents on Computer Science, vol.4, pp.147-176, 2011. ,
DOI : 10.2174/1874479611104030147
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos, Computer Vision and Image Understanding, vol.122, pp.4-21, 2014. ,
DOI : 10.1016/j.cviu.2013.12.005
A survey on moving object detection methods in video surveillance, International Bulletin of mathematical research, pp.2019-218, 2015. ,
A survey on approaches of object detection, International journal of Computer Applications, vol.65, issue.18, pp.14-20, 2013. ,
A Survey on Moving Object Tracking in Video, International Journal on Information Theory, vol.3, issue.3, pp.31-46, 2014. ,
DOI : 10.5121/ijit.2014.3304
A survey on object detection and tracking methods, International Journal of Innovative Research in Computer and Communication Engineering, vol.2, issue.2, pp.2970-2978, 2014. ,
Survey on moving object detection, International Journal of Modern Trends in Engineering and Research, vol.2, issue.11, pp.285-289, 2015. ,
A survey on moving object detection in static and dynamic background for automated video analysis, International Journal for Scientific Research & Development, vol.1, issue.10, pp.2050-2054, 2013. ,
On seeing stuff: the perception of materials by humans and machines, In Human Vision and Electronic Imaging, pp.1-12, 2001. ,
EigenTracking: Robust matching and tracking of articulated objects using a view-based representation, International Journal of Computer Vision, vol.26, issue.1, pp.63-84, 1998. ,
DOI : 10.1007/BFb0015548
A real-time color-based object tracking robust to irregular illumination variations, IEEE International Conference on Robotics and Automation, pp.1659-1664, 2001. ,
Moving object tracking under varying illumination conditions, Pattern recognition letters, pp.1632-1643, 2006. ,
DOI : 10.1016/j.patrec.2006.03.010
A texture-based method for modeling the background and detecting moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.657-662, 2006. ,
DOI : 10.1109/TPAMI.2006.68
Object tracking under illumination variations using 2D-cepstrum characteristics of the target, 2010 IEEE International Workshop on Multimedia Signal Processing, pp.521-526, 2010. ,
DOI : 10.1109/MMSP.2010.5662076
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity, IEEE Transactions on Image Processing, vol.24, issue.1, pp.359-373, 2015. ,
DOI : 10.1109/TIP.2014.2378053
Scene conditional background update for moving object detection in a moving camera, Pattern Recognition Letters, vol.88, pp.57-63, 2017. ,
DOI : 10.1016/j.patrec.2017.01.017
Incremental learning for visual tracking, Advances in neural information processing systems, pp.793-800, 2004. ,
Covariance Tracking using Model Update Based on Lie Algebra, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.728-735, 2006. ,
DOI : 10.1109/CVPR.2006.94
URL : http://www.merl.com/papers/docs/TR2005-127.pdf
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.758-765, 2006. ,
DOI : 10.1109/CVPR.2006.52
URL : http://www.cs.brown.edu/people/black/Papers/balan06roam.pdf
Learning Motion Patterns in Videos, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.531-539, 2017. ,
DOI : 10.1109/CVPR.2017.64
URL : https://hal.archives-ouvertes.fr/hal-01427480
Tracking of abrupt motion using Wang-Landau Monte Carlo estimation, European Conference on Computer Vision, pp.387-400, 2008. ,
Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling, IEEE Transactions on Image Processing, vol.21, issue.2, pp.789-801, 2012. ,
DOI : 10.1109/TIP.2011.2168414
Hamiltonian Monte Carlo estimator for abrupt motion tracking, International Conference on Pattern Recognition (ICPR), pp.3066-3069, 2012. ,
Extended kernel correlation filter for abrupt motion tracking, KSII Transactions on Internet & Information Systems, vol.11, issue.9, pp.4438-4460, 2017. ,
Robust online appearance models for visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.1296-1311, 2003. ,
DOI : 10.1109/TPAMI.2003.1233903
URL : http://www.cs.toronto.edu/~tem/cvpr01.pdf
Contour-based object tracking with occlusion handling in video acquired using mobile cameras, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1531-1536, 2004. ,
DOI : 10.1109/TPAMI.2004.96
Appearance models for occlusion handling, Image and Vision Computing, vol.24, issue.11, pp.1233-1243, 2006. ,
DOI : 10.1016/j.imavis.2005.06.007
URL : http://www.cvg.rdg.ac.uk/PETS2001/PETSFINALPDF/senior.pdf
Robust Occlusion Handling in Object Tracking, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. ,
DOI : 10.1109/CVPR.2007.383453
Robust Human Tracking Based on DPM Constrained Multiple-Kernel from a Moving Camera, Journal of Signal Processing Systems, vol.31, issue.10, pp.27-39, 2017. ,
DOI : 10.1109/CVPR.2013.312
Background modelling and subtraction of dynamic scenes, Ninth IEEE International Conference on Computer Vision, pp.1305-1312, 2003. ,
Statistical Modeling of Complex Backgrounds for Foreground Object Detection, IEEE Transactions on Image Processing, vol.13, issue.11, pp.1459-1472, 2004. ,
DOI : 10.1109/TIP.2004.836169
A Brief Survey of Dynamic Texture Description and Recognition, Computer Recognition Systems, pp.17-26, 2005. ,
DOI : 10.1007/3-540-32390-2_2
Dynamic texture representation using a deep multi-scale convolutional network, Journal of Visual Communication and Image Representation, pp.43-89, 2017. ,
Adaptive background model registration for moving cameras, Pattern Recognition Letters, vol.96, pp.86-95, 2017. ,
DOI : 10.1016/j.patrec.2017.03.010
Detecting moving shadows: algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.7, pp.918-923, 2003. ,
DOI : 10.1109/TPAMI.2003.1206520
URL : http://cvrr.ucsd.edu/publications/2003/PAMIJuly2003.pdf
Shadow detection: A survey and comparative evaluation of recent methods, Pattern Recognition, vol.45, issue.4, pp.1684-1695, 2012. ,
DOI : 10.1016/j.patcog.2011.10.001
A survey of cast shadow detection algorithms, Pattern Recognition Letters, vol.33, issue.6, pp.752-764, 2012. ,
DOI : 10.1016/j.patrec.2011.12.013
A survey on Shadow Detection and Removal in images and video sequences, 2016 6th International Conference, Cloud System and Big Data Engineering (Confluence), pp.518-523, 2016. ,
DOI : 10.1109/CONFLUENCE.2016.7508175
A modified Gaussian mixture background model via spatiotemporal distribution with shadow detection, Signal, Image and Video Processing, pp.343-350, 2016. ,
DOI : 10.1007/s11760-014-0747-z
A Shadow Elimination Algorithm Based on HSV Spatial Feature and Texture Feature, International Conference on Emerging Internetworking, Data & Web Technologies, pp.585-591, 2017. ,
DOI : 10.1109/TPAMI.2008.150
Real-time object tracking for soccer-robots without color information, Robotics and Autonomous Systems, vol.48, issue.1, pp.41-48, 2004. ,
DOI : 10.1016/j.robot.2004.05.005
Enhanced background subtraction using global motion compensation and mosaicing, 2008 15th IEEE International Conference on Image Processing, pp.2708-2711, 2008. ,
DOI : 10.1109/ICIP.2008.4712353
Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans (PAMI, pp.30-1728, 2008. ,
Detecting, tracking and counting fish in low quality unconstrained underwater videos, pp.514-519, 2008. ,
Fast Feature Pyramids for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, pp.1532-1545, 2014. ,
DOI : 10.1109/TPAMI.2014.2300479
A Robust Tracking System for Low Frame Rate Video, International Journal of Computer Vision, vol.13, issue.9, pp.279-304, 2015. ,
DOI : 10.1109/TIP.2004.836152
URL : http://eprints.bbk.ac.uk/13597/1/13597.pdf
PLS-CCA heterogeneous features fusion-based low-resolution human detection method for outdoor video surveillance, International Journal of Automation and Computing, vol.28, issue.11, pp.136-146, 2017. ,
DOI : 10.1109/TPAMI.2006.217
Statistical mosaics for tracking, Image and Vision Computing, vol.14, issue.8, pp.549-564, 1996. ,
DOI : 10.1016/0262-8856(96)01103-1
Statistical background modeling for non-stationary camera, Pattern Recognition Letters, vol.24, issue.1-3, pp.183-196, 2003. ,
DOI : 10.1016/S0167-8655(02)00210-6
A keypoint-based method for background modeling and foreground detection using a PTZ camera, Pattern Recognition Letters, vol.96, pp.96-96, 2017. ,
DOI : 10.1016/j.patrec.2016.10.015
Multiple View Geometry in Computer Vision, 2003. ,
DOI : 10.1017/CBO9780511811685
Recovery of ego-motion using region alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.3, pp.268-272, 1997. ,
DOI : 10.1109/34.584105
A unified approach to moving object detection in 2D and 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.6, pp.577-589, 1998. ,
DOI : 10.1109/34.683770
Independent motion detection in 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.10, pp.1191-1199, 2000. ,
DOI : 10.1109/34.879803
URL : http://www.sarnoff.com/career_move/tech_papers/pdf/main_iccv99_final.pdf
Moving object detection and segmentation in urban environments from a moving platform, Image and Vision Computing, vol.68, pp.68-76, 2017. ,
DOI : 10.1016/j.imavis.2017.07.006
URL : https://hal.archives-ouvertes.fr/hal-01609038
Representing moving images with layers, IEEE Transactions on Image Processing, vol.3, issue.5, pp.625-638, 1994. ,
DOI : 10.1109/83.334981
URL : http://www-bcs.mit.edu/people/adelson/./publications/acrobat/wang/tr279.pdf
Motion layer extraction in the presence of occlusion using graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1644-1659, 2005. ,
DOI : 10.1109/TPAMI.2005.202
Object-Level Motion Detection From Moving Cameras, IEEE Transactions on Circuits and Systems for Video Technology, vol.27, issue.11, pp.2333-2343, 2017. ,
DOI : 10.1109/TCSVT.2016.2587387
Staristical background subtraction for a mobile observer, IEEE International Conference on Computer Vision (ICCV), pp.67-74, 2003. ,
DOI : 10.1109/iccv.2003.1238315
URL : http://www.nada.kth.se/~hayman/ICCV2003/HaymanEklundhICCV2003.pdf
Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework, IEEE Transactions on Consumer Electronics, vol.55, issue.3, pp.1714-1721, 2009. ,
DOI : 10.1109/TCE.2009.5278047
A Moving Object Detection Method Adapted to Camera Jittering, Journal of Electronics and Information Technology, vol.35, issue.8, pp.1914-1920, 2014. ,
Video stabilization for a hand-held camera based on 3D motion model, 2009 16th IEEE International Conference on Image Processing (ICIP), pp.3477-3480, 2009. ,
DOI : 10.1109/ICIP.2009.5413831
A novel digital image stabilization for mobile applications, 2011 IEEE International Conference on Consumer Electronics (ICCE), pp.209-210, 2011. ,
DOI : 10.1109/ICCE.2011.5722544
Estimating pose of articulated objects using low-level motion, Computer Vision and Image Understanding, vol.116, issue.3, pp.330-346, 2012. ,
DOI : 10.1016/j.cviu.2011.08.007
DART: dense articulated real-time tracking with consumer depth cameras, Autonomous Robots, vol.33, issue.5, pp.239-258, 2015. ,
DOI : 10.1109/CVPR.2014.301
Highly non-rigid video object tracking using segmentbased object candidates. Multimedia Tools and Applications, pp.9565-9586, 2017. ,
DOI : 10.1007/s11042-016-3563-3
Robust object region detection in natural video using motion estimation and region-based diffusion, 2004. ,
Evaluation of background subtraction algorithms with postprocessing, Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp.192-199, 2008. ,
Frame differencing with post-processing techniques for moving object detection in outdoor environment, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, pp.172-176, 2011. ,
DOI : 10.1109/CSPA.2011.5759867
Multi target tracking by linking tracklets with a convolutional neural network (VISIGRAPP, pp.492-498, 2017. ,
DOI : 10.5220/0006155204920498
Learning to segment moving objects in videos, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4083-4090, 2015. ,
DOI : 10.1109/CVPR.2015.7299035
URL : http://arxiv.org/pdf/1412.6504
A video stabilization method based on inter-frame image matching score, Global Journal of Computer Science and Technology, vol.17, issue.1, 2017. ,
Real-time tracking of non-rigid objects using mean shift, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.142-149, 2000. ,
DOI : 10.1109/CVPR.2000.854761
A real time adaptive visual surveillance system for tracking low-resolution color targets in dynamically changing scenes, Image and Vision Computing, vol.21, issue.10, pp.913-929, 2003. ,
Real-time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes, IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), pp.970-975, 2005. ,
Tracking Dynamic Objects in a RoboCup Environment-The Attempto Tübingen Robot Soccer Team, RoboCup-2003: Robot Soccer World Cup VII, Lecture Notes in Computer Science, 2003. ,
Real-Time Tracking via On-line Boosting, Procedings of the British Machine Vision Conference 2006, p.6, 2006. ,
DOI : 10.5244/C.20.6
URL : http://www.bmva.org/bmvc/2006/papers/033.pdf
A Real-Time Object Tracking System Using a Particle Filter, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.2822-2827, 2006. ,
DOI : 10.1109/IROS.2006.282066
Automated Visual Surveillance in Realistic Scenarios, IEEE Multimedia, vol.14, issue.1, pp.30-39, 2007. ,
DOI : 10.1109/MMUL.2007.3
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors, Proceeding of 10th European Conference on Computer Vision, pp.831-844, 2008. ,
DOI : 10.5244/C.19.64
A real-time object detecting and tracking system for outdoor night surveillance, Pattern Recognition, vol.41, issue.1, pp.432-444, 2008. ,
DOI : 10.1016/j.patcog.2007.05.017
Adaptive pyramid mean shift for global real-time visual tracking, Image and Vision Computing, vol.28, issue.3, pp.424-437, 2010. ,
DOI : 10.1016/j.imavis.2009.06.012
Adaptive Color Attributes for Real-Time Visual Tracking, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1090-1097, 2014. ,
DOI : 10.1109/CVPR.2014.143
URL : http://liu.diva-portal.org/smash/get/diva2:711538/FULLTEXT01.pdf
Real-Time* Multiple Object Tracking (MOT) for Autonomous Navigation, 2017) (cs231n.stanford, p.630, 2017. ,
Effective and Efficient Detection of Moving Targets From a UAV???s Camera, IEEE Transactions on Intelligent Transportation Systems, vol.19, issue.2, pp.497-506, 2018. ,
DOI : 10.1109/TITS.2017.2782790
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking, 2017 IEEE International Conference on Computer Vision (ICCV), 2017. ,
DOI : 10.1109/ICCV.2017.128
Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, vol.27, issue.7, pp.773-780, 2006. ,
DOI : 10.1016/j.patrec.2005.11.005
Detection of Moving Objects with Non-stationary Cameras in 5.8ms: Bringing Motion Detection to Your Mobile Device, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.27-34, 2013. ,
DOI : 10.1109/CVPRW.2013.9
Statistical moving object detection for mobile devices with camera, 2015 IEEE International Conference on Consumer Electronics (ICCE), pp.15-16, 2015. ,
DOI : 10.1109/ICCE.2015.7066301
URL : http://oa.upm.es/37674/1/INVE_MEM_2015_202125.pdf
Detection of moving objects in a video captured by a moving camera using error reduction, SICE Annual Conference, pp.347-352, 2014. ,
Background modelling from a free-moving camera by multi-layer homography algorithm, IEEE International Conference on Image Processing (ICIP), pp.1572-1575, 2008. ,
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
URL : http://rainsoft.de/publications/iv11b.pdf
Evaluation of foreground detection methodology for a moving camera, 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), pp.1-4, 2015. ,
DOI : 10.1109/FCV.2015.7103752
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, IEEE Transactions on Image Processing, vol.17, issue.7, pp.1168-1177, 2008. ,
DOI : 10.1109/TIP.2008.924285
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras, arXiv preprint, 2017. ,
Moving object detection using background subtraction for a moving camera with pronounced parallax, 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2017. ,
DOI : 10.1109/SDF.2017.8126361
Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction, IEEE Transactions on Circuits and Systems for Video Technology, vol.27, issue.2, pp.236-248, 2017. ,
DOI : 10.1109/TCSVT.2015.2493499
Online codebook modelling based background subtraction with a moving camera, 3rd International Conference on Frontiers of Signal Processing (ICFSP), pp.136-140, 2017. ,
DOI : 10.1109/icfsp.2017.8097157
Changedetection. net: A new change detection benchmark dataset, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1-8, 2012. ,
A deep convolutional neural network for background subtraction, 2017. ,
Background Subtraction for Freely Moving Cameras, 2009 IEEE 12th International Conference on Computer Vision, pp.1219-1225, 2009. ,
DOI : 10.1109/ICCV.2009.5459334
Trajectory aligned features for first person action recognition, Pattern Recognition, vol.62, pp.45-55, 2017. ,
DOI : 10.1016/j.patcog.2016.07.031
URL : http://arxiv.org/pdf/1604.02115
Tracking Persons-of-Interest via Unsupervised Representation Adaptation, arXiv preprint, 2017. ,
Robust principal component analysis?, Journal of the ACM, vol.58, issue.3, p.11, 2011. ,
DOI : 10.1145/1970392.1970395
Robust salient motion detection in non-stationary videos via novel integrated strategies of spatio-temporal coherency clues and low-rank analysis, Pattern Recognition, vol.52, pp.52-410, 2016. ,
DOI : 10.1016/j.patcog.2015.09.033
Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modelling, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1844-1852, 2017. ,
Augmented robust PCA for foregroundbackground separation on noisy, moving camera video, 2017. ,
OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage, IEEE Transactions on Information Theory, vol.60, issue.5, pp.3002-3018, 2014. ,
DOI : 10.1109/TIT.2014.2311661
URL : http://arxiv.org/pdf/1306.6042.pdf
Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis, IEEE Transactions on Circuits and Systems I: Regular Papers, vol.65, issue.3, pp.1003-1015, 2018. ,
DOI : 10.1109/TCSI.2017.2758379
Struck: Structured Output Tracking with Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.10, pp.2096-2109, 2016. ,
DOI : 10.1109/TPAMI.2015.2509974
Real-Time Compressive Tracking, European conference on computer vision, pp.864-877, 2012. ,
DOI : 10.1007/978-3-642-33712-3_62
URL : http://faculty.ucmerced.edu/mhyang/papers/eccv12_ct.pdf
Adaptive Color Attributes for Real-Time Visual Tracking, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1090-1097, 2014. ,
DOI : 10.1109/CVPR.2014.143
URL : http://liu.diva-portal.org/smash/get/diva2:711538/FULLTEXT01.pdf
Geometric Hypergraph Learning for Visual Tracking, IEEE Transactions on Cybernetics, vol.47, issue.12, pp.4182-4195, 2017. ,
DOI : 10.1109/TCYB.2016.2626275
URL : http://arxiv.org/pdf/1603.05930
On the role and the importance of features for background modeling and foreground detection, Computer Science Review, vol.28, pp.26-91, 2018. ,
DOI : 10.1016/j.cosrev.2018.01.004
URL : https://hal.archives-ouvertes.fr/hal-01724317
Differential Earth Mover's Distance with Its Applications to Visual Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.2, pp.274-287 ,
DOI : 10.1109/TPAMI.2008.299
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
A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005. ,
DOI : 10.1109/TPAMI.2005.188
URL : https://hal.archives-ouvertes.fr/inria-00548227
SURF: Speeded up robust features, Computer vision (ECCV), pp.404-417, 2006. ,
DOI : 10.1007/11744023_32
Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004. ,
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps
Comparison of Local Feature Descriptors, 2006. ,
A comparative study of the performance of local feature-based pattern recognition algorithms, Pattern Analysis and Applications, pp.1145-1156, 2017. ,
Multiple object tracking using SIFT features and location matching, International Journal of Smart Home, vol.5, issue.4, pp.17-26, 2011. ,
Multiple Hypothesis Tracking Revisited, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4696-4704, 2015. ,
DOI : 10.1109/ICCV.2015.533
Learning by Tracking: Siamese CNN for Robust Target Association, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.33-40, 2016. ,
DOI : 10.1109/CVPRW.2016.59
Hierarchical Convolutional Features for Visual Tracking, 2015 IEEE International Conference on Computer Vision (ICCV), pp.3074-3082, 2015. ,
DOI : 10.1109/ICCV.2015.352
Adaptive background mixture models for real-time tracking, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp.246-252, 1999. ,
DOI : 10.1109/CVPR.1999.784637
Moving object tracking using Gaussian mixture model and optical flow, International Journal of Advanced Research in Computer Science and Software Engineering, vol.3, issue.4, pp.243-246, 2013. ,
Fast object tracking with long-term occlusions handling in dynamic scenes, 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp.823-827, 2014. ,
DOI : 10.1109/ICRoM.2014.6991006
ROBUST OBJECT TRACKING USING JOINT COLOR-TEXTURE HISTOGRAM, International Journal of Pattern Recognition and Artificial Intelligence, vol.24, issue.07, pp.1245-1263, 2009. ,
DOI : 10.1109/TPAMI.2007.1110
Robust and accurate object tracking under various types of occlusions, IEEE Transactions on Circuits and Systems for Video Technology, vol.18, issue.2, pp.223-236, 2008. ,
Object tracking using deformable templates, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp.544-549, 2000. ,
DOI : 10.1109/ICCV.1998.710756
Adaptive Object Tracking by Learning Hybrid Template Online, IEEE Transactions On Circuits and Systems For Video Technology, vol.21, issue.11, pp.1588-1599, 2011. ,
Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.564-575, 2003. ,
DOI : 10.1109/TPAMI.2003.1195991
Robust Object Tracking with Online Multiple Instance Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.8, pp.1619-1632, 2011. ,
DOI : 10.1109/TPAMI.2010.226
URL : http://faculty.ucmerced.edu/mhyang/papers/pami11b.pdf
Regularized least-square object tracking based on ?2, 1 minimization, 3rd RSI International Conference on Robotics and Mechatronics (ICROM), pp.535-539, 2015. ,
DOI : 10.1109/icrom.2015.7367857
Robust trackingby-detection using a detector confidence particle filter, IEEE 12th International Conference on Computer Vision, pp.1515-1522, 2009. ,
DOI : 10.1109/iccv.2009.5459278
URL : http://www.mmp.rwth-aachen.de/publications/pdf/breitenstein-detectorconfidencefilter-iccv09.pdf
Multi-Target Tracking by Discrete-Continuous Energy Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.10, pp.2054-2068, 2016. ,
DOI : 10.1109/TPAMI.2015.2505309
Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection, European Conference on Computer Vision, pp.43-51, 2016. ,
DOI : 10.1109/TIP.2014.2324292
URL : https://infoscience.epfl.ch/record/221401/files/Le_BMTTECCVWORKSHOP_2016.pdf
Enhancing Detection Model for Multiple Hypothesis Tracking, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.2143-2152, 2017. ,
DOI : 10.1109/CVPRW.2017.266
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies, 2017 IEEE International Conference on Computer Vision (ICCV), p.6, 2017. ,
DOI : 10.1109/ICCV.2017.41
Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle, Pattern Recognition, vol.48, issue.10, pp.2964-2982, 2015. ,
DOI : 10.1016/j.patcog.2015.02.012
Hierarchical Convolutional Features for Visual Tracking, 2015 IEEE International Conference on Computer Vision (ICCV), pp.3074-3082, 2015. ,
DOI : 10.1109/ICCV.2015.352
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking, IEEE Transactions on Image Processing, vol.25, issue.4, pp.1834-1848, 2016. ,
DOI : 10.1109/TIP.2015.2510583
URL : http://arxiv.org/pdf/1503.00072
Learning Multi-domain Convolutional Neural Networks for Visual Tracking, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4293-4302, 2016. ,
DOI : 10.1109/CVPR.2016.465
URL : http://arxiv.org/pdf/1510.07945
Learning a deep compact image representation for visual tracking, Advances in neural information processing systems, pp.809-817, 2013. ,
Transferring rich feature hierarchies for robust visual tracking, 2015. ,
Video Tracking Using Learned Hierarchical Features, IEEE Transactions on Image Processing, vol.24, issue.4, pp.1424-1435, 2015. ,
DOI : 10.1109/TIP.2015.2403231
Visual Tracking with Fully Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), pp.3119-3127, 2015. ,
DOI : 10.1109/ICCV.2015.357
Deep learning of appearance models for online object tracking, 2016. ,
Deep visual tracking: Review and experimental comparison, Pattern Recognition, vol.76, pp.323-338, 2018. ,
DOI : 10.1016/j.patcog.2017.11.007
A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. ,
DOI : 10.1109/CVPR.2007.382974
Online Moving Camera Background Subtraction, European Conference on Computer Vision, pp.228-241, 2012. ,
DOI : 10.1007/978-3-642-33783-3_17
URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.9850&rep=rep1&type=pdf
Modeling and segmentation of floating foreground and background in videos, Pattern Recognition, vol.45, issue.4, pp.1696-1706, 2012. ,
DOI : 10.1016/j.patcog.2011.10.018
Generalized background subtraction based on hybrid inference by belief propagation and Bayesian filtering, IEEE Conference on Computer Vision, pp.2174-2181, 2011. ,
Particle video: long-range motion estimation using point trajectories, IEEE Conference on Computer Vision and Pattern Recognition, pp.2195-2202, 2006. ,
DOI : 10.1109/cvpr.2006.219
URL : http://rvsn.csail.mit.edu/pv/pv-ijcv.pdf
Background Subtraction Using Low Rank and Group Sparsity Constraints, European Conference on Computer Vision, pp.612-625, 2012. ,
DOI : 10.1007/978-3-642-33718-5_44
URL : http://www.research.rutgers.edu/~shaoting/paper/ECCV12-background.pdf
Background subtraction for the moving camera: A geometric approach, Computer Vision and Image Understanding, vol.127, pp.73-85, 2014. ,
DOI : 10.1016/j.cviu.2014.06.007
CDnet 2014: An Expanded Change Detection Benchmark Dataset, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.387-394, 2014. ,
DOI : 10.1109/CVPRW.2014.126
URL : https://hal.archives-ouvertes.fr/hal-01018757
A survey of datasets for visual tracking, Machine Vision and Applications, pp.23-52, 2016. ,
DOI : 10.1016/j.patcog.2012.11.013
URL : https://hal.archives-ouvertes.fr/hal-01217152
Robust Bilayer Segmentation and Motion/Depth Estimation with a Handheld Camera, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.603-617, 2011. ,
DOI : 10.1109/TPAMI.2010.115
Learning color and locality cues for moving object detection and segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp.320-327, 2009. ,
The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.34, issue.11, pp.2015-98 ,
DOI : 10.1109/TPAMI.2012.204
Performance evaluation of multi-target tracking using the OSPA metric, 2010 13th International Conference on Information Fusion, pp.1-7, 2010. ,
DOI : 10.1109/ICIF.2010.5712055
A Metric for Performance Evaluation of Multi-Target Tracking Algorithms, IEEE Transactions on Signal Processing, vol.59, issue.7, pp.3452-3457, 2011. ,
DOI : 10.1109/TSP.2011.2140111
Performance evaluation of object detection algorithms for video surveillance, IEEE Transactions on Multimedia, vol.8, issue.4, pp.761-774, 2006. ,
DOI : 10.1109/TMM.2006.876287
An open development environment for evaluation of video surveillance systems, Third IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp.32-39, 2002. ,
Tools and techniques for video performance evaluation, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.167-170, 2000. ,
DOI : 10.1109/ICPR.2000.902888
A novel method for video tracking performance evaluation, IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp.125-132, 2003. ,
Fusion-based foreground enhancement for background subtraction using multivariate multi-model Gaussian distribution, Information Sciences, vol.430, issue.431, pp.430-414, 2018. ,
DOI : 10.1016/j.ins.2017.11.062
A Multi-transformational Model for Background Subtraction with Moving Cameras, Computer Vision (ECCV), pp.817-819, 2014. ,
DOI : 10.1007/978-3-319-10590-1_52
A 3-dimensional sift descriptor and its application to action recognition, Proceedings of the 15th international conference on Multimedia , MULTIMEDIA '07, pp.357-360, 2017. ,
DOI : 10.1145/1291233.1291311
A Spatio-Temporal Descriptor Based on 3D-Gradients, Procedings of the British Machine Vision Conference 2008, pp.275-276, 2008. ,
DOI : 10.5244/C.22.99
URL : https://hal.archives-ouvertes.fr/inria-00514853
An efficient dense and scale-invariant spatiotemporal interest point detector, European conference on computer vision, pp.650-663, 2008. ,
DOI : 10.1007/978-3-540-88688-4_48