Robust Fragments-based Tracking using the Integral Histogram, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), p.17, 2006. ,
DOI : 10.1109/CVPR.2006.256
Measuring the Objectness of Image Windows, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2189-2202, 2012. ,
DOI : 10.1109/TPAMI.2012.28
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002. ,
DOI : 10.1109/78.978374
Support vector tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.8, pp.1064-1072, 2004. ,
DOI : 10.1109/TPAMI.2004.53
Ensemble Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.2, pp.261-271, 2007. ,
DOI : 10.1109/TPAMI.2007.35
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
Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues, 2007 IEEE 11th International Conference on Computer Vision, p.50, 2007. ,
DOI : 10.1109/ICCV.2007.4408955
Randomized Ensemble Tracking, 2013 IEEE International Conference on Computer Vision, p.10, 2013. ,
DOI : 10.1109/ICCV.2013.255
A Superior Tracking Approach: Building a Strong Tracker through Fusion, ECCV ,
DOI : 10.1007/978-3-319-10584-0_12
Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004. ,
DOI : 10.1023/B:VISI.0000011205.11775.fd
Curriculum learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.32, 2009. ,
DOI : 10.1145/1553374.1553380
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
Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, p.13, 1998. ,
DOI : 10.1145/279943.279962
Correlation Filters for Object Alignment, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
DOI : 10.1109/CVPR.2013.297
Average of Synthetic Exact Filters, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206701
Simple real-time human detection using a single correlation filter, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2009. ,
DOI : 10.1109/PETS-WINTER.2009.5399555
Visual object tracking using adaptive correlation filters, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5539960
Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm, Microprocessor research labs, p.17, 2001. ,
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004. ,
DOI : 10.1109/TPAMI.2004.60
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, p.78, 2001. ,
DOI : 10.1109/ICCV.2001.937505
Object Segmentation by Long Term Analysis of Point Trajectories, ECCV, p.78, 2010. ,
DOI : 10.1007/978-3-642-15555-0_21
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.500-513, 2011. ,
DOI : 10.1109/TPAMI.2010.143
A review of visual tracking, Dept. Comput. Sci. Eng, p.10, 2008. ,
Constrained parametric min-cuts for automatic object segmentation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.50, 2010. ,
DOI : 10.1109/CVPR.2010.5540063
Is my new tracker really better than yours? In WACV, pp.2014-2053 ,
Visual object tracking performance measures revisited. arXiv preprint ,
An experimental survey on correlation filter-based tracking. arXiv preprint, 2015. ,
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.95, 2014. ,
DOI : 10.1109/CVPR.2014.268
URL : https://hal.archives-ouvertes.fr/hal-01053675
Thirteen Hard Cases in Visual Tracking, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, p.35, 2010. ,
DOI : 10.1109/AVSS.2010.85
A real-time computer vision system for vehicle tracking and traffic surveillance, Transportation Research Part C: Emerging Technologies, vol.6, issue.4, pp.271-288, 1998. ,
DOI : 10.1016/S0968-090X(98)00019-9
A system for video surveillance and monitoring, 2000. ,
Online selection of discriminative tracking features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1631-1643, 2005. ,
DOI : 10.1109/TPAMI.2005.205
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.5-17, 2000. ,
DOI : 10.1109/CVPR.2000.854761
Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.564-577, 2003. ,
DOI : 10.1109/TPAMI.2003.1195991
Kalman filter for vision tracking, p.18, 2005. ,
Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.50-75 ,
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512
Accurate Scale Estimation for Robust Visual Tracking, Proceedings of the British Machine Vision Conference 2014, pp.57-58 ,
DOI : 10.5244/C.28.65
Adaptive Color Attributes for Real-Time Visual Tracking, 2014 IEEE Conference on Computer Vision and Pattern Recognition ,
DOI : 10.1109/CVPR.2014.143
Learning Spatially Regularized Correlation Filters for Visual Tracking, 2015 IEEE International Conference on Computer Vision (ICCV), 2015. ,
DOI : 10.1109/ICCV.2015.490
Characterizing image motion, Dept. Comput. Sci, issue.5, 2006. ,
Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, vol.89, issue.1-2, pp.31-71, 1997. ,
DOI : 10.1016/S0004-3702(96)00034-3
Context tracker: Exploring supporters and distracters in unconstrained environments, CVPR 2011, pp.22-23, 2011. ,
DOI : 10.1109/CVPR.2011.5995733
Structured Forests for Fast Edge Detection, 2013 IEEE International Conference on Computer Vision, pp.6-53, 2013. ,
DOI : 10.1109/ICCV.2013.231
Long-term recurrent convolutional networks for visual recognition and description, CVPR, p.97, 2015. ,
FlowNet: Learning Optical Flow with Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), p.97, 2015. ,
DOI : 10.1109/ICCV.2015.316
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking, ECCV, pp.24-50, 2008. ,
DOI : 10.1007/978-3-540-88688-4_17
PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects, 2013 IEEE International Conference on Computer Vision, pp.2013-2029 ,
DOI : 10.1109/ICCV.2013.308
URL : https://hal.archives-ouvertes.fr/hal-00976387
Probabilistic tracking in joint feature-spatial spaces, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., p.17, 2003. ,
DOI : 10.1109/CVPR.2003.1211432
Taking the bite out of automated naming of characters in TV video, Image and Vision Computing, vol.27, issue.5, pp.545-559, 2009. ,
DOI : 10.1016/j.imavis.2008.04.018
The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, pp.303-338, 2010. ,
DOI : 10.1007/s11263-009-0275-4
LIBLINEAR: A library for large linear classification, The Journal of Machine Learning Research, vol.9, issue.56, pp.1871-1874, 2008. ,
One-shot learning of object categories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.594-611, 2006. ,
DOI : 10.1109/TPAMI.2006.79
The thermal infrared visual object tracking vot- tir2015 challenge results, ICCV Workshop on Visual Object Tracking Challenge, pp.36-42, 2015. ,
Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010. ,
DOI : 10.1109/TPAMI.2009.167
The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol.21, issue.1, pp.32-40, 1975. ,
DOI : 10.1109/TIT.1975.1055330
Transfer Learning Based Visual Tracking with Gaussian Processes Regression, ECCV, p.10, 2014. ,
DOI : 10.1007/978-3-319-10578-9_13
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 1997. ,
DOI : 10.1109/CVPR.2014.81
URL : http://arxiv.org/abs/1311.2524
Hough-based tracking of non-rigid objects, ICCV, pp.46-72, 2011. ,
Real-Time Tracking via On-line Boosting, Procedings of the British Machine Vision Conference 2006, pp.12-14, 2006. ,
DOI : 10.5244/C.20.6
Semi-supervised On-Line Boosting for Robust Tracking, ECCV, pp.46-72, 2008. ,
DOI : 10.1007/978-3-540-88682-2_19
Tracking the invisible: Learning where the object might be, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.22, 2010. ,
DOI : 10.1109/CVPR.2010.5539819
Competitive Learning: From Interactive Activation to Adaptive Resonance, Cognitive Science, vol.1, issue.1, pp.23-63, 1987. ,
DOI : 10.1111/j.1551-6708.1987.tb00862.x
Some network flow problems solved with pseudo-boolean programming, Operations Research, vol.13, issue.3, pp.388-399, 1965. ,
Kernel-based bayesian filtering for object tracking, CVPR, p.18, 2005. ,
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering, 2007 IEEE 11th International Conference on Computer Vision, 1926. ,
DOI : 10.1109/ICCV.2007.4408938
Struck: Structured output tracking with kernels, ICCV, pp.61-71, 2011. ,
W/sup 4/: real-time surveillance of people and their activities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.809-830, 2000. ,
DOI : 10.1109/34.868683
Multiple view geometry in computer vision, pp.77-80, 2004. ,
DOI : 10.1017/CBO9780511811685
Exploiting the Circulant Structure of Tracking-by-Detection with Kernels, ECCV, pp.20-22, 2012. ,
DOI : 10.1007/978-3-642-33765-9_50
High-Speed Tracking with Kernelized Correlation Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.3, pp.583-596, 2015. ,
DOI : 10.1109/TPAMI.2014.2345390
Putting Objects in Perspective, International Journal of Computer Vision, vol.57, issue.2, pp.3-15, 2008. ,
DOI : 10.1007/s11263-008-0137-5
Online tracking by learning discriminative saliency map with convolutional neural network, ICML, 2015a. 28 and 29 ,
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.34 ,
DOI : 10.1109/CVPR.2015.7298675
What Makes for Effective Detection Proposals?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.4, pp.814-830, 2016. ,
DOI : 10.1109/TPAMI.2015.2465908
Method and means for recognizing complex patterns, pp.654-52, 1962. ,
Occlusion and Motion Reasoning for Long-Term Tracking, ECCV, pp.50-61, 2014. ,
DOI : 10.1007/978-3-319-10599-4_12
URL : https://hal.archives-ouvertes.fr/hal-01020149
Online Object Tracking with Proposal Selection, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2015-2022 ,
DOI : 10.1109/ICCV.2015.354
URL : https://hal.archives-ouvertes.fr/hal-01207196
Condensation ? Conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998. ,
DOI : 10.1023/A:1008078328650
Icondensation: Unifying low-level and high-level tracking in a stochastic framework, ECCV, 1950. ,
DOI : 10.1007/BFb0055711
Visual tracking via adaptive structural local sparse appearance model, CVPR, pp.2012-2060 ,
Forward-Backward Error: Automatic Detection of Tracking Failures, 2010 20th International Conference on Pattern Recognition, p.17, 2010. ,
DOI : 10.1109/ICPR.2010.675
Tracking-Learning-Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, pp.1409-1422, 2012. ,
DOI : 10.1109/TPAMI.2011.239
A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960. ,
DOI : 10.1115/1.3662552
Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988. ,
DOI : 10.1007/BF00133570
What energy functions can be minimized via graph cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004. ,
DOI : 10.1109/TPAMI.2004.1262177
The Visual Object Tracking VOT2013 Challenge Results, 2013 IEEE International Conference on Computer Vision Workshops, p.36, 2013. ,
DOI : 10.1109/ICCVW.2013.20
The Visual Object Tracking VOT2014 Challenge Results, ECCV Workshop on Visual Object Tracking Challenge, pp.45-47, 2014. ,
DOI : 10.1007/978-3-319-16181-5_14
URL : https://hal.archives-ouvertes.fr/hal-01301090
The visual object tracking VOT2015 challenge results, ICCV Workshop on Visual Object Tracking Challenge, pp.36-40, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01336773
A Novel Performance Evaluation Methodology for Single-Target Trackers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.11, p.12, 2016. ,
DOI : 10.1109/TPAMI.2016.2516982
ImageNet classification with deep convolutional neural networks, NIPS, 2012. 26 and 97 ,
Self-paced learning for latent variable models, NIPS, p.32, 2010. ,
A hierarchical field framework for unified context-based classification, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 1921. ,
DOI : 10.1109/ICCV.2005.9
Multihypothesis trajectory analysis for robust visual tracking, CVPR, 2015. 24 and 95 ,
A General Framework for Combining Visual Trackers ??? The "Black Boxes" Approach, International Journal of Computer Vision, vol.67, issue.3, pp.343-363, 2006. ,
DOI : 10.1007/s11263-006-5568-2
Semi-supervised boosting using visual similarity learning, 2008 IEEE Conference on Computer Vision and Pattern Recognition, p.13, 2008. ,
DOI : 10.1109/CVPR.2008.4587629
Fast normalized cross-correlation. Vision Interface, pp.120-123, 1995. ,
Track to the future: Spatio-temporal video segmentation with long-range motion cues, CVPR 2011, p.76, 2011. ,
DOI : 10.1109/CVPR.2011.6044588
URL : https://hal.archives-ouvertes.fr/hal-00817961
NUS-PRO: A New Visual Tracking Challenge, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.2, pp.335-349, 2015. ,
DOI : 10.1109/TPAMI.2015.2417577
Video Segmentation by Tracking Many Figure-Ground Segments, 2013 IEEE International Conference on Computer Vision, pp.2013-95 ,
DOI : 10.1109/ICCV.2013.273
DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, Proceedings of the British Machine Vision Conference 2014, p.97, 2014. ,
DOI : 10.5244/C.28.56
A survey of appearance models in visual object tracking, ACM Transactions on Intelligent Systems and Technology, vol.4, issue.4, p.58, 0210. ,
DOI : 10.1145/2508037.2508039
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration, ECCV Workshop on Visual Object Tracking Challenge, 2014. ,
DOI : 10.1007/978-3-319-16181-5_18
Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1921. ,
DOI : 10.1109/CVPR.2015.7298632
Real-time part-based visual tracking via adaptive correlation filters, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ,
DOI : 10.1109/CVPR.2015.7299124
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
An iterative image registration technique with an application to stereo vision, IJCAI, 1981. ,
Multiple object tracking: A literature review ,
Hierarchical Convolutional Features for Visual Tracking, 2015 IEEE International Conference on Computer Vision (ICCV), 2015. ,
DOI : 10.1109/ICCV.2015.352
Long-term correlation tracking, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ,
DOI : 10.1109/CVPR.2015.7299177
Minimum average correlation energy filters, Applied Optics, vol.26, issue.17, pp.3633-3640, 1987. ,
DOI : 10.1364/AO.26.003633
Unconstrained correlation filters, Applied Optics, vol.33, issue.17, pp.3751-3759, 1994. ,
DOI : 10.1364/AO.33.003751
Ensemble of exemplar-SVMs for object detection and beyond, 2011 International Conference on Computer Vision, p.81, 2011. ,
DOI : 10.1109/ICCV.2011.6126229
SemiBoost: Boosting for Semi-Supervised Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.312000-2014, 2009. ,
DOI : 10.1109/TPAMI.2008.235
A novel method for tracking and counting pedestrians in real-time using a single camera, IEEE Transactions on Vehicular Technology, vol.50, issue.5, pp.1267-1278, 2001. ,
DOI : 10.1109/25.950328
The template update problem, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.6, pp.810-815, 2004. ,
DOI : 10.1109/TPAMI.2004.16
Robust visual tracking using 1 minimization, ICCV, p.72, 2009. ,
Minimum error bounded efficient 1 tracker with occlusion detection, CVPR, pp.11-49, 2011. ,
Contextual classification with functional Max-Margin Markov Networks, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.22, 2009. ,
DOI : 10.1109/CVPR.2009.5206590
Using the forest to see the trees: A graphical model relating features, objects and scenes, NIPS, p.22, 2003. ,
Learning Multi-domain Convolutional Neural Networks for Visual Tracking, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997. ,
DOI : 10.1109/CVPR.2016.465
Consensus-based matching and tracking of keypoints for object tracking, IEEE Winter Conference on Applications of Computer Vision ,
DOI : 10.1109/WACV.2014.6836013
Clustering of static-adaptive correspondences for deformable object tracking, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2015-95 ,
DOI : 10.1109/CVPR.2015.7298895
Segmentation of Moving Objects by Long Term Video Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.6, pp.1187-1200, 2014. ,
DOI : 10.1109/TPAMI.2013.242
Finding the Best from the Second Bests - Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms, 2013 IEEE International Conference on Computer Vision ,
DOI : 10.1109/ICCV.2013.346
Robust visual tracking using autoregressive hidden Markov model, CVPR, p.50, 2012. ,
Real-time face detection and tracking for mobile videoconferencing. Real-Time Imaging, pp.81-94, 2004. ,
DOI : 10.1016/j.rti.2004.02.004
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.7578
Visual interpretation of hand gestures for human-computer interaction: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.677-695, 1997. ,
DOI : 10.1109/34.598226
Color-Based Probabilistic Tracking, ECCV, p.10, 2002. ,
DOI : 10.1007/3-540-47969-4_44
Data Fusion for Visual Tracking With Particles, Proceedings of the IEEE, pp.495-513, 1926. ,
DOI : 10.1109/JPROC.2003.823147
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers, pp.61-74, 1999. ,
In defense of color-based model-free tracking, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2015-2025 ,
DOI : 10.1109/CVPR.2015.7298823
Objects in Context, 2007 IEEE 11th International Conference on Computer Vision, p.22, 2007. ,
DOI : 10.1109/ICCV.2007.4408986
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS, pp.2015-97 ,
DOI : 10.1109/TPAMI.2016.2577031
Deep convolutional matching. arXiv preprint ,
EpicFlow: Edgepreserving interpolation of correspondences for optical flow, CVPR, pp.2015-95 ,
URL : https://hal.archives-ouvertes.fr/hal-01142656
Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision, vol.61, issue.3, pp.125-141, 2008. ,
DOI : 10.1007/s11263-007-0075-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.4310
"GrabCut", ACM Transactions on Graphics, vol.23, issue.3, pp.309-314, 2004. ,
DOI : 10.1145/1015706.1015720
ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015. ,
DOI : 10.1007/s11263-015-0816-y
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
PROST: Parallel robust online simple tracking, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. ,
DOI : 10.1109/CVPR.2010.5540145
Finding Trajectories of Feature Points in a Monocular Image Sequence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.56-73, 1987. ,
DOI : 10.1109/TPAMI.1987.4767872
Good features to track, CVPR, p.17, 1994. ,
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation, ECCV, p.22, 2006. ,
DOI : 10.1007/11744023_1
Real-time human pose recognition in parts from single depth images, Communications of the ACM, vol.56, issue.1, pp.116-124, 2013. ,
DOI : 10.1145/2398356.2398381
Part-based multiple-person tracking with partial occlusion handling, CVPR, p.10, 2012. ,
The MPEG-4 video standard verification model, IEEE Transactions on Circuits and Systems for Video Technology, vol.7, issue.1, pp.19-31, 1997. ,
DOI : 10.1109/76.554415
Two-stream convolutional networks for action recognition in videos, NIPS, pp.2014-97 ,
Very deep convolutional networks for large-scale image recognition ,
Deep inside convolutional networks: Visualising image classification models and saliency maps, ICLR Workshop, pp.2014-2043 ,
Visual tracking: An experimental survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.12, pp.1442-1468, 2014. ,
Tracking-by-Segmentation with Online Gradient Boosting Decision Tree, 2015 IEEE International Conference on Computer Vision (ICCV), p.95, 2015. ,
DOI : 10.1109/ICCV.2015.350
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines, 2013 IEEE International Conference on Computer Vision ,
DOI : 10.1109/ICCV.2013.36
Contextualizing object detection and classification, CVPR 2011, p.22, 2011. ,
DOI : 10.1109/CVPR.2011.5995330
Towards robust multi-cue integration for visual tracking, Machine Vision and Applications, pp.50-58, 1926. ,
Learning to track with multiple observers, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1926. ,
DOI : 10.1109/CVPR.2009.5206634
Dense point trajectories by gpuaccelerated large displacement optical flow, ECCV, pp.74-76, 2010. ,
Self-Paced Learning for Long-Term Tracking, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.61-75, 2013. ,
DOI : 10.1109/CVPR.2013.308
Real-time image tracking for automatic traffic monitoring and enforcement applications, Image and Vision Computing, vol.22, issue.6, pp.485-501, 2004. ,
DOI : 10.1016/j.imavis.2003.12.001
DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014 IEEE Conference on Computer Vision and Pattern Recognition ,
DOI : 10.1109/CVPR.2014.220
Co-tracking using semisupervised support vector machines, ICCV, pp.10-13, 2007. ,
SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm, Computer Graphics Forum, p.95, 2012. ,
DOI : 10.1111/j.1467-8659.2012.03013.x
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1 11, pp.267-288, 1996. ,
Detection and tracking of point features, pp.5-17, 1991. ,
Contextual priming for object detection, International Journal of Computer Vision, vol.53, issue.2, pp.169-191, 2003. ,
DOI : 10.1023/A:1023052124951
Motion Coherent Tracking Using Multi-label MRF Optimization, International Journal of Computer Vision, vol.27, issue.10, pp.190-202, 2012. ,
DOI : 10.1007/s11263-011-0512-5
Large margin methods for structured and interdependent output variables, In Journal of Machine Learning Research, vol.6, pp.1453-1484, 2005. ,
Learning Color Names for Real-World Applications, IEEE Transactions on Image Processing, vol.18, issue.7, pp.1512-1523, 1921. ,
DOI : 10.1109/TIP.2009.2019809
URL : https://hal.archives-ouvertes.fr/inria-00439284
Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.10-95, 2001. ,
DOI : 10.1109/CVPR.2001.990517
Robust scale-adaptive mean-shift for tracking, Pattern Recognition Letters, vol.49, pp.250-258, 2014. ,
DOI : 10.1016/j.patrec.2014.03.025
Visual Tracking with Fully Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), 1997. ,
DOI : 10.1109/ICCV.2015.357
Learning a deep compact image representation for visual tracking, NIPS, p.27, 2013. ,
Ensemble-based tracking: Aggregating crowdsourced structured time series data, ICML ,
Transferring rich feature hierarchies for robust visual tracking, 1928. ,
Intelligent multi-camera video surveillance: A review, Pattern Recognition Letters, vol.34, issue.1, pp.3-19, 2013. ,
DOI : 10.1016/j.patrec.2012.07.005
Signal???to???Noise Improvement and the Statistics of Track Populations, Journal of Applied Physics, vol.26, issue.5, pp.586-595, 1955. ,
DOI : 10.1063/1.1722046
Learning to detect Motion Boundaries, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2015-2021 ,
DOI : 10.1109/CVPR.2015.7298873
URL : https://hal.archives-ouvertes.fr/hal-01142653
Online Spatio-temporal Structural Context Learning for Visual Tracking, ECCV, pp.2012-2035 ,
DOI : 10.1007/978-3-642-33765-9_51
JOTS: Joint Online Tracking and Segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.95, 2015. ,
DOI : 10.1109/CVPR.2015.7298835
Whose vote should count more: Optimal integration of labels from labelers of unknown expertise, NIPS, p.25, 2009. ,
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
Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2009. ,
DOI : 10.1109/TPAMI.2008.79
Online Object Tracking: A Benchmark, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.61-75, 2013. ,
DOI : 10.1109/CVPR.2013.312
Object Tracking Benchmark, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.9, pp.1834-1848 ,
DOI : 10.1109/TPAMI.2014.2388226
Efficient mean-shift tracking via a new similarity measure, CVPR, p.17, 2005. ,
Recent advances and trends in visual tracking: A review, Neurocomputing, vol.74, issue.18, pp.3823-3831, 2011. ,
DOI : 10.1016/j.neucom.2011.07.024
Context-Aware Visual Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.7, pp.1195-1209, 2009. ,
DOI : 10.1109/TPAMI.2008.146
Object tracking, ACM Computing Surveys, vol.38, issue.4, pp.1-45, 2006. ,
DOI : 10.1145/1177352.1177355
Visualizing and Understanding Convolutional Networks, ECCV, p.29, 2014. ,
DOI : 10.1007/978-3-319-10590-1_53
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization, ECCV, 2014a. 32, 46, and 95 ,
DOI : 10.1007/978-3-319-10599-4_13
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.666.3510
Fast Visual Tracking via Dense Spatio-temporal Context Learning, ECCV, 2014b. 21 and 23 ,
DOI : 10.1007/978-3-319-10602-1_9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.640.545
Sparse coding based visual tracking: Review and experimental comparison, Pattern Recognition, vol.46, issue.7, pp.1772-1788, 2013. ,
DOI : 10.1016/j.patcog.2012.10.006
Low-Rank Sparse Learning for Robust Visual Tracking, ECCV, pp.2012-2023 ,
DOI : 10.1007/978-3-642-33783-3_34
Robust Visual Tracking via Structured Multi-Task Sparse Learning, CVPR, pp.2012-2023 ,
DOI : 10.1007/s11263-012-0582-z
Visual tracking via weakly supervised learning from multiple imperfect oracles, Pattern Recognition, vol.47, issue.3, pp.1395-1410, 2014. ,
DOI : 10.1016/j.patcog.2013.10.002
Robust object tracking via sparsitybased collaborative model, CVPR, pp.48-61, 2012. ,
An ensemble of deep neural networks for object tracking, 2014 IEEE International Conference on Image Processing (ICIP), pp.2014-2041 ,
DOI : 10.1109/ICIP.2014.7025169
Tracking randomly moving objects on edge box proposals. arXiv preprint ,
Edge Boxes: Locating Object Proposals from Edges, ECCV, 2014. 6, pp.54-101 ,
DOI : 10.1007/978-3-319-10602-1_26