Learning Local-Global Multi-Graph Descriptors for RGB-T Object Tracking
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Jian Zhang | Bin Luo | Chenglong Li | Chengli Zhu | Jin Tang | Xiaohao Wu | Jian Zhang | B. Luo | Jin Tang | Chenglong Li | Chengli Zhu | Xiaohao Wu
[1] Kaihua Zhang,et al. Visual Tracking via Nonlocal Similarity Learning , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[2] Rui Caseiro,et al. Ieee Transactions on Pattern Analysis and Machine Intelligence High-speed Tracking with Kernelized Correlation Filters , 2022 .
[3] Jin Young Choi,et al. Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Dorin Comaniciu,et al. Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[5] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[6] Changsheng Xu,et al. Multi-task Correlation Particle Filter for Robust Object Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Michael Felsberg,et al. Adaptive Color Attributes for Real-Time Visual Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Jin Tang,et al. ReGLe: Spatially Regularized Graph Learning for Visual Tracking , 2017, ACM Multimedia.
[9] Stefan Duffner,et al. PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects , 2013, ICCV.
[10] Hejun Wu,et al. Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection , 2017, IEEE Transactions on Circuits and Systems for Video Technology.
[11] Bernard Ghanem,et al. Context-Aware Correlation Filter Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Yiannis Demiris,et al. Attentional Correlation Filter Network for Adaptive Visual Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Huchuan Lu,et al. Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[14] Jinqiao Wang,et al. Adversarial Deep Tracking , 2019, IEEE Transactions on Circuits and Systems for Video Technology.
[15] Rynson W. H. Lau,et al. Visual Tracking via Locality Sensitive Histograms , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[16] Simon Lucey,et al. Learning Background-Aware Correlation Filters for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[17] Jin Tang,et al. Weighted Sparse Representation Regularized Graph Learning for RGB-T Object Tracking , 2017, ACM Multimedia.
[18] Horst Bischof,et al. Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.
[19] Michael Felsberg,et al. Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[21] Huchuan Lu,et al. Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Yong Yu,et al. Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Stan Sclaroff,et al. MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.
[24] Zdenek Kalal,et al. Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Wotao Yin,et al. A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion , 2013, SIAM J. Imaging Sci..
[26] Zhixun Su,et al. Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation , 2011, NIPS.
[27] Dit-Yan Yeung,et al. Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.
[28] Luca Bertinetto,et al. End-to-End Representation Learning for Correlation Filter Based Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Liang Lin,et al. Visual Tracking via Dynamic Graph Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Han-Ul Kim,et al. SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[31] Erik Blasch,et al. Encoding color information for visual tracking: Algorithms and benchmark , 2015, IEEE Transactions on Image Processing.
[32] Michael Felsberg,et al. Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.
[33] Huchuan Lu,et al. Robust Superpixel Tracking , 2014, IEEE Transactions on Image Processing.
[34] Ming-Hsuan Yang,et al. Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Shuicheng Yan,et al. SOLD: Sub-optimal low-rank decomposition for efficient video segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Xiaojie Guo,et al. Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix , 2015, IJCAI.
[37] Jin Tang,et al. RGB-T Object Tracking: Benchmark and Baseline , 2018, Pattern Recognit..
[38] Xiaoqin Zhang,et al. Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Jin Tang,et al. Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[40] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[41] Liang Lin,et al. Learning Patch-Based Dynamic Graph for Visual Tracking , 2017, AAAI.
[42] Rynson W. H. Lau,et al. CREST: Convolutional Residual Learning for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[43] Ming-Hsuan Yang,et al. Long-term correlation tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[45] Ming-Hsuan Yang,et al. Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Yang Lu,et al. Online Object Tracking, Learning and Parsing with And-Or Graphs , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[47] Fuchun Sun,et al. Fusion tracking in color and infrared images using joint sparse representation , 2012, Science China Information Sciences.
[48] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[49] Bin Luo,et al. Fast Grayscale-Thermal Foreground Detection With Collaborative Low-Rank Decomposition , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[50] Luca Bertinetto,et al. Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Liang Lin,et al. An Approach to Streaming Video Segmentation With Sub-Optimal Low-Rank Decomposition , 2016, IEEE Transactions on Image Processing.
[52] Liang Lin,et al. Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.
[53] David Zhang,et al. Fast Visual Tracking via Dense Spatio-temporal Context Learning , 2014, ECCV.
[54] Ming-Hsuan Yang,et al. Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[55] Wenhan Luo,et al. Multiple object tracking: A literature review , 2014, Artif. Intell..
[56] Jin Tang,et al. Real-Time Grayscale-Thermal Tracking via Laplacian Sparse Representation , 2016, MMM.
[57] Michael Felsberg,et al. ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).