16.6.2.5.1 Target Tracking Techniques, Kernel Tracking

Chapter Contents (Back)
Target Tracking. Kernel Trackers. Also:
See also Target Tracking Techniques, Filter Techniques, Correlation.
See also Target Tracking Techniques, Kalman Filters.

Martinez, B.[Brais], Binefa, X.[Xavier],
Piecewise affine kernel tracking for non-planar targets,
PR(41), No. 12, December 2008, pp. 3682-3691.
Elsevier DOI 0810
Kernel-based tracking; Multiple kernel; SSD error; Visual tracking; Parametric motion BibRef

Martinez, B.[Brais], Vivet, M.[Marc], Binefa, X.[Xavier],
Compatible Particles for Part-Based Tracking,
AMDO10(1-10).
Springer DOI 1007
BibRef

Vivet, M.[Marc], Martínez, B.[Brais], Binefa, X.[Xavier],
Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation,
IbPRIA09(144-151).
Springer DOI 0906
BibRef

Orriols, X., Binefa, X.[Xavier],
Classifying periodic motions in video sequences,
ICIP03(I: 945-948).
IEEE DOI 0312
BibRef

Martínez, B., Pérez, A., Ferraz, L., Binefa, X.[Xavier],
Structure Restriction for Tracking Through Multiple Views and Occlusions,
IbPRIA07(I: 121-128).
Springer DOI 0706
BibRef

Martinez, B., Ferraz, L., Binefa, X., Diaz-Caro, J.,
Multiple Kernel Two-Step Tracking,
ICIP06(2785-2788).
IEEE DOI 0610
BibRef

Yang, M.[Ming], Fan, Z.M.[Zhi-Min], Fan, J., Wu, Y.[Ying],
Tracking Nonstationary Visual Appearances by Data-Driven Adaptation,
IP(18), No. 7, July 2009, pp. 1633-1644.
IEEE DOI 0906
BibRef

Yang, M.[Ming], Wu, Y.[Ying],
Tracking Non-Stationary Appearances and Dynamic Feature Selection,
CVPR05(II: 1059-1066).
IEEE DOI 0507
BibRef

Fan, Z.M.[Zhi-Min], Yang, M.[Ming], Wu, Y.[Ying], Hua, G.[Gang], Yu, T.[Ting],
Efficient Optimal Kernel Placement for Reliable Visual Tracking,
CVPR06(I: 658-665).
IEEE DOI 0606
BibRef

Fan, Z.M.[Zhi-Min], Yang, M.[Ming], Wu, Y.[Ying],
Multiple Collaborative Kernel Tracking,
PAMI(29), No. 7, July 2007, pp. 1268-1273.
IEEE DOI 0706
BibRef
Earlier: CVPR05(II: 502-509).
IEEE DOI 0507
For motion fields that may not be recoverable from image measurements. Use local motion info. Scale and rotation changes. BibRef

Yang, M.[Ming], Yu, T.[Ting], Wu, Y.[Ying],
Game-Theoretic Multiple Target Tracking,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Yang, M.[Ming], Wu, Y.[Ying], Lao, S.H.[Shi-Hong],
Mining Auxiliary Objects for Tracking by Multibody Grouping,
ICIP07(III: 361-364).
IEEE DOI 0709
BibRef
Earlier:
Intelligent Collaborative Tracking by Mining Auxiliary Objects,
CVPR06(I: 697-704).
IEEE DOI 0606
BibRef

Yang, M.[Ming], Wu, Y.[Ying], Hua, G.[Gang],
Context-Aware Visual Tracking,
PAMI(31), No. 7, July 2009, pp. 1195-1209.
IEEE DOI 0905
Consider context in tracking. Integrate into tracking objects discovered by data mining. These are persistant co-occurance and motion with the target, easy to track.
See also Mining Compositional Features From GPS and Visual Cues for Event Recognition in Photo Collections. BibRef

Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Mining Visual Collocation Patterns via Self-Supervised Subspace Learning,
SMC-B(42), No. 2, April 2012, pp. 334-346.
IEEE DOI 1204
BibRef

Yuan, J.S.[Jun-Song], Wu, Y.[Ying], Yang, M.[Ming],
Discovery of Collocation Patterns: from Visual Words to Visual Phrases,
CVPR07(1-8).
IEEE DOI 0706
bag-of-words. BibRef

Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Spatial Random Partition for Common Visual Pattern Discovery,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Yuan, J.S.[Jun-Song], Li, Z.[Zhu], Fu, Y.[Yun], Wu, Y.[Ying], Huang, T.S.[Thomas S.],
Common Spatial Pattern Discovery by Efficient Candidate Pruning,
ICIP07(I: 165-168).
IEEE DOI 0709
BibRef

Zivkovic, Z.[Zoran], Cemgil, A.T.[Ali Taylan], Krose, B.J.A.[Ben J.A.],
Approximate Bayesian methods for kernel-based object tracking,
CVIU(113), No. 6, June 2009, pp. 743-749.
Elsevier DOI 0904
BibRef
Earlier: A1, A3, Only:
An EM-like algorithm for color-histogram-based object tracking,
CVPR04(I: 798-803).
IEEE DOI 0408
Object tracking; Approximate Bayesian filtering BibRef

Zivkovic, Z.[Zoran], van der Heijden, F.[Ferdinand],
Better features to track by estimating the tracking convergence region,
ICPR02(II: 635-638).
IEEE DOI 0211
BibRef

Miao, Q.[Quan], Wang, G.J.[Gui-Jin], Shi, C.B.[Chen-Bo], Lin, X.G.[Xing-Gang], Ruan, Z.W.[Zhi-Wei],
A new framework for on-line object tracking based on SURF,
PRL(32), No. 13, 1 October 2011, pp. 1564-1571.
Elsevier DOI 1109
Object tracking; Keypoint matching; On-line boosting; Adaptive classifiers; SURF BibRef

Miao, Q.[Quan], Wang, G.J.[Gui-Jin], Lin, X.G.[Xing-Gang],
Implementation of Scale and Rotation Invariant On-Line Object Tracking Based on CUDA,
IEICE(E94-D), No. 12, December 2011, pp. 2549-2552.
WWW Link. 1112
BibRef

Miao, Q.[Quan], Wang, G.J.[Gui-Jin], Lin, X.G.[Xing-Gang],
Kernel-Based On-Line Object Tracking Combining both Local Description and Global Representation,
IEICE(E96-D), No. 1, January 2013, pp. 159-162.
WWW Link. 1301
BibRef

Miao, Q.[Quan], Wang, G.J.[Gui-Jin], Lin, X.G.[Xing-Gang], Wang, Y.M.[Yong-Ming], Shi, C.B.[Chen-Bo], Liao, C.[Chao],
Scale and rotation invariant feature-based object tracking via modified on-line boosting,
ICIP10(3929-3932).
IEEE DOI 1009
BibRef

Li, G.R.[Guo-Rong], Qu, W.[Wei], Huang, Q.M.[Qing-Ming],
A Multiple Targets Appearance Tracker Based on Object Interaction Models,
CirSysVideo(22), No. 3, March 2012, pp. 450-464.
IEEE DOI 1203
BibRef
Earlier:
Real-time interactive multi-target tracking using kernel-based trackers,
ICIP10(689-692).
IEEE DOI 1009
BibRef

Wang, Z.J.[Zhi-Jie], Ben Salah, M.[Mohamed], Zhang, H.[Hong], Ray, N.[Nilanjan],
Shape based appearance model for kernel tracking,
IVC(30), No. 4-5, May 2012, pp. 332-344.
Elsevier DOI 1206
Object tracking; Appearance model; Shape cue BibRef

Wang, Z.J.[Zhi-Jie], Zhang, H.[Hong], Ray, N.[Nilanjan],
Tracking of multiple interacting objects using a novel prediction model,
ICIP09(869-872).
IEEE DOI 0911
BibRef

Papoutsakis, K.E.[Konstantinos E.], Argyros, A.A.[Antonis A.],
Integrating tracking with fine object segmentation,
IVC(31), No. 10, 2013, pp. 771-785.
Elsevier DOI 1310
BibRef
Earlier:
Object Tracking and Segmentation in a Closed Loop,
ISVC10(I: 405-416).
Springer DOI 1011
Kernel-based object tracking BibRef

Kosmopoulos, D.[Dimitrios], Papoutsakis, K.E.[Konstantinos E.], Argyros, A.A.[Antonis A.],
A Framework for Online Segmentation and Classification of Modeled Actions Performed in the Context of Unmodeled Ones,
CirSysVideo(27), No. 12, December 2017, pp. 2578-2590.
IEEE DOI 1712
BibRef
Earlier:
Segmentation and classification of modeled actions in the context of unmodeled ones,
BMVC14(xx-yy).
HTML Version. 1410
Computational modeling, Dynamic programming, Hidden Markov models, Support vector machines, action spotting BibRef

Zeng, F.X.[Fan-Xiang], Liu, X.[Xuan], Huang, Z.T.[Zhi-Tong], Ji, Y.F.[Yue-Feng],
Kernel Based Multiple Cue Adaptive Appearance Model For Robust Real-time Visual Tracking,
SPLetters(20), No. 11, 2013, pp. 1094-1097.
IEEE DOI 1310
Bayes methods BibRef

Zoidi, O., Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Visual Object Tracking Based on Local Steering Kernels and Color Histograms,
CirSysVideo(23), No. 5, May 2013, pp. 870-882.
IEEE DOI 1305
BibRef

Liu, F.H.[Fang-Hui], Zhou, T.[Tao], Fu, K.[Keren], Yang, J.[Jie],
Kernelized temporal locality learning for real-time visual tracking,
PRL(90), No. 1, 2017, pp. 72-79.
Elsevier DOI 1704
Visual tracking BibRef

Li, Y.[Yang], Zhang, Y.F.[Ya-Fei], Xu, Y.L.[Yu-Long], Wang, J.B.[Jia-Bao], Miao, Z.[Zhuang],
Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features,
SPLetters(23), No. 8, August 2016, pp. 1136-1140.
IEEE DOI 1608
filtering theory BibRef

Liu, Z.G.[Zhong-Geng], Lian, Z.C.[Zhi-Chao], Li, Y.[Yang],
A novel adaptive kernel correlation filter tracker with multiple feature integration,
ICIP17(2572-2576)
IEEE DOI 1803
Correlation, Estimation, Feature extraction, Image color analysis, Robustness, Target tracking, Visualization, Visual Tracking BibRef

Li, Y.[Yang], Zhu, J.K.[Jian-Ke],
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration,
VOT14(254-265).
Springer DOI 1504
BibRef

Zhang, B.C.[Bao-Chang], Li, Z.G.[Zhi-Gang], Cao, X., Ye, Q., Chen, C., Shen, L., Perina, A.[Alessandro], Jill, R.,
Output Constraint Transfer for Kernelized Correlation Filter in Tracking,
SMCS(47), No. 4, April 2017, pp. 693-703.
IEEE DOI 1704
Benchmark testing BibRef

Gao, L.[Long], Li, Y.S.[Yun-Song], Ning, J.F.[Ji-Feng],
Improved kernelized correlation filter tracking by using spatial regularization,
JVCIR(50), No. 1, 2018, pp. 74-82.
Elsevier DOI 1712
Visual tracking BibRef

Chen, K.[Kai], Tao, W.B.[Wen-Bing],
Convolutional Regression for Visual Tracking,
IP(27), No. 7, July 2018, pp. 3611-3620.
IEEE DOI 1805
Discriminatively Learned Correlation Filters (DCF). Convolution, Correlation, Feature extraction, Kernel, Predictive models, Training, Visualization, Visual tracking, linear regression BibRef

Zheng, W.W.[Wei-Wei], Yu, H.M.[Hui-Min], Huang, W.[Wei],
Visual tracking via Graph Regularized Kernel Correlation Filer and Multi-Memory Voting,
JVCIR(55), 2018, pp. 688-697.
Elsevier DOI 1809
Visual object tracking, Graph Regularized Kernel Correlation Filer, Drift handling BibRef

Wang, J.[Jun], Liu, W.B.[Wei-Bin], Xing, W.W.[Wei-Wei], Zhang, S.[Shunli],
Visual object tracking with multi-scale superpixels and color-feature guided kernelized correlation filters,
SP:IC(63), 2018, pp. 44-62.
Elsevier DOI 1804
Visual tracking, Superpixel, Kernelized correlation filter, Bayesian filter, Color feature guided confidence map, Multi-scale superpixels BibRef

Yao, R.[Rui], Xia, S.X.[Shi-Xiong], Shen, F.[Fumin], Zhou, Y.[Yong], Niu, Q.[Qiang],
Exploiting Spatial Structure from Parts for Adaptive Kernelized Correlation Filter Tracker,
SPLetters(23), No. 5, May 2016, pp. 658-662.
IEEE DOI 1604
Computational modeling BibRef

Zhang, H.L.[Huan-Long], Zhang, X.J.[Xiu-Jiao], Wang, Y.[Yong], Qian, X.L.[Xiao-Liang], Wang, Y.F.[Yan-Feng],
Extended cuckoo search-based kernel correlation filter for abrupt motion tracking,
IET-CV(12), No. 6, September 2018, pp. 763-769.
DOI Link 1808
BibRef

Li, C.[Ce], Liu, X.C.[Xing-Chao], Su, X.B.[Xiang-Bo], Zhang, B.C.[Bao-Chang],
Robust kernelized correlation filter with scale adaption for real-time single object tracking,
RealTimeIP(14), No. 3, October 2018, pp. 583-596.
WWW Link. 1811
BibRef

Uzkent, B., Rangnekar, A., Hoffman, M.J.,
Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters,
GeoRS(57), No. 1, January 2019, pp. 449-461.
IEEE DOI 1901
Hyperspectral imaging, Tracking, Computer architecture, Correlation, Videos, Training, Deep features, hyperspectral sensing, vehicle tracking BibRef

Ruan, W.J.[Wei-Jian], Chen, J.[Jun], Wu, Y.[Yi], Wang, J.Q.[Jin-Qiao], Liang, C.[Chao], Hu, R.M.[Rui-Min], Jiang, J.J.[Jun-Jun],
Multi-Correlation Filters With Triangle-Structure Constraints for Object Tracking,
MultMed(21), No. 5, May 2019, pp. 1122-1134.
IEEE DOI 1905
correlation methods, filtering theory, object detection, object tracking, target tracking, multiple CFs, global object, high integrity BibRef

Tang, M.[Ming], Yu, B.[Bin], Zhang, F.[Fan], Wang, J.Q.[Jin-Qiao],
High-Speed Tracking with Multi-kernel Correlation Filters,
CVPR18(4874-4883)
IEEE DOI 1812
Kernel, Correlation, Linear programming, Target tracking, Upper bound, Training BibRef

Tang, M.[Ming], Feng, J.Y.[Jia-Yi],
Multi-kernel Correlation Filter for Visual Tracking,
ICCV15(3038-3046)
IEEE DOI 1602
Correlation BibRef

Gong, L.[Lin], Mo, Z.C.[Zhen-Chong], Zhao, S.N.[Shang-Nan], Song, Y.[Yong],
An improved Kernelized Correlation Filter tracking algorithm based on multi-channel memory model,
SP:IC(78), 2019, pp. 200-205.
Elsevier DOI 1909
Multi-channel memory, Kernelized Correlation Filter, Target tracking BibRef

Shao, J., Du, B., Wu, C., Zhang, L.,
Can We Track Targets From Space? A Hybrid Kernel Correlation Filter Tracker for Satellite Video,
GeoRS(57), No. 11, November 2019, pp. 8719-8731.
IEEE DOI 1911
Target tracking, Satellites, Correlation, Feature extraction, Streaming media, Kernel, Histogram of oriented gradient (HOG), satellite video tracking BibRef

Mbelwa, J.T.[Jimmy T.], Zhao, Q.J.[Qing-Jie], Wang, F.S.[Fa-Sheng],
Visual tracking tracker via object proposals and co-trained kernelized correlation filters,
VC(36), No. 6, June 2020, pp. 1173-1187.
WWW Link. 2005
BibRef

Fan, B., Cong, Y., Tian, J., Tang, Y.,
Reliable Multi-Kernel Subtask Graph Correlation Tracker,
IP(29), 2020, pp. 8120-8133.
IEEE DOI 2008
Target tracking, Correlation, Kernel, Feature extraction, Robustness, Laplace equations, Layered multi-subtask learning, object tracking BibRef

Wang, J., Zheng, L., Tang, M., Feng, J.,
A Comparison of Correlation Filter-Based Trackers and Struck Trackers,
CirSysVideo(30), No. 9, September 2020, pp. 3106-3118.
IEEE DOI 2009
Correlation, Target tracking, Kernel, Support vector machines, Training, Optimization, Discrete Fourier transforms, ranking SVM tracker BibRef

Nousi, P.[Paraskevi], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Dense convolutional feature histograms for robust visual object tracking,
IVC(99), 2020, pp. 103933.
Elsevier DOI 2006
Object Tracking, Deep Learning, Bag-of-Features, Convolutional Feature Histograms BibRef

Nousi, P.[Paraskevi], Triantafyllidou, D.[Danai], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Re-identification framework for long term visual object tracking based on object detection and classification,
SP:IC(88), 2020, pp. 115969.
Elsevier DOI 2009
Visual object tracking, Long-term tracking, Re-detection, Deep learning BibRef

Wang, B.[Botao], Xiong, H.K.[Hong-Kai], Jiang, X.Q.[Xiao-Qian], Zheng, Y.F.,
Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition,
IP(23), No. 4, April 2014, pp. 1765-1778.
IEEE DOI 1404
computer vision BibRef

Li, X.[Xi], Dick, A.[Anthony], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Wang, H.Z.[Han-Zi],
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking,
PAMI(35), No. 4, April 2013, pp. 863-881.
IEEE DOI 1303
BibRef
Earlier: A1, A2, A5, A3, A4:
Graph mode-based contextual kernels for robust SVM tracking,
ICCV11(1156-1163).
IEEE DOI 1201
Tracking with more than binary constraints. BibRef

Li, X.[Xi], Dick, A., Shen, C.H.[Chun-Hua], Zhang, Z.F.[Zhong-Fei], van den Hengel, A.J., Wang, H.Z.[Han-Zi],
Visual Tracking With Spatio-Temporal Dempster-Shafer Information Fusion,
IP(22), No. 8, 2013, pp. 3028-3040.
IEEE DOI 1307
Bayes methods; SVM classifier; visual tracking BibRef

Wu, Q.Q.[Qiang-Qiang], Yan, Y.[Yan], Liang, Y.J.[Yan-Jie], Liu, Y.[Yi], Wang, H.Z.[Han-Zi],
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking,
ACCV18(V:119-134).
Springer DOI 1906
BibRef

Liu, F.[Fayao], Shen, C.H.[Chun-Hua], Reid, I.D.[Ian D.], van den Hengel, A.J.[Anton J.],
Online unsupervised feature learning for visual tracking,
IVC(51), No. 1, 2016, pp. 84-94.
Elsevier DOI 1606
Object tracking BibRef

Li, X.[Xi], Shen, C.H.[Chun-Hua], Dick, A.[Anthony], van den Hengel, A.J.[Anton J.],
Learning Compact Binary Codes for Visual Tracking,
CVPR13(2419-2426)
IEEE DOI 1309
SVM; Visual Tracking; appearance model; hashing; hypergraph BibRef

Kumar, P.[Pankaj], Dick, A.[Anthony], Brooks, M.J.[Michael J.],
Multiple target tracking with an efficient compact colour correlogram,
ICARCV08(699-704).
IEEE DOI 1109
BibRef
And:
Integrated Bayesian multi-cue tracker for objects observed from moving cameras,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef
Earlier: A1, A3, A2:
Adaptive Multiple Object Tracking Using Colour and Segmentation Cues,
ACCV07(I: 853-863).
Springer DOI 0711
BibRef

Kumar, P.[Pankaj], Dick, A.[Anthony],
Adaptive earth movers distance-based Bayesian multi-target tracking,
IET-CV(7), No. 4, 2013, pp. 246-257.
DOI Link 1307
BibRef

Li, X.[Xi], Shen, C.H.[Chun-Hua], Dick, A.[Anthony], Zhang, Z.F.M.[Zhong-Fei Mark], Zhuang, Y.T.[Yue-Ting],
Online Metric-Weighted Linear Representations for Robust Visual Tracking,
PAMI(38), No. 5, May 2016, pp. 931-950.
IEEE DOI 1604
feature extraction BibRef

Li, X.[Xi], Shen, C.H.[Chun-Hua], Shi, Q.F.[Qin-Feng], Dick, A.[Anthony], van den Hengel, A.J.[Anton J.],
Non-sparse linear representations for visual tracking with online reservoir metric learning,
CVPR12(1760-1767).
IEEE DOI 1208
BibRef

Yao, R.[Rui],
Robust Model-Free Multi-Object Tracking with Online Kernelized Structural Learning,
SPLetters(22), No. 12, December 2015, pp. 2401-2405.
IEEE DOI 1512
learning (artificial intelligence) BibRef

Yao, R.[Rui], Shi, Q.F.[Qin-Feng], Shen, C.H.[Chun-Hua], Zhang, Y.N.[Yan-Ning], van den Hengel, A.J.[Anton J.],
Part-Based Robust Tracking Using Online Latent Structured Learning,
CirSysVideo(27), No. 6, June 2017, pp. 1235-1248.
IEEE DOI 1706
BibRef
Earlier:
Part-Based Visual Tracking with Online Latent Structural Learning,
CVPR13(2363-2370)
IEEE DOI 1309
BibRef
Earlier:
Robust Tracking with Weighted Online Structured Learning,
ECCV12(III: 158-172).
Springer DOI 1210
Computational modeling, Deformable models, Object tracking, Robustness, Support vector machines, Target tracking, Visualization, Online latent structured learning, part-based model, visual tracking. online structural learning; BibRef

Kumar, P.[Pankaj], Brooks, M.J.[Michael J.], van den Hengel, A.J.[Anton J.],
An Adaptive Bayesian Technique for Tracking Multiple Objects,
PReMI07(657-665).
Springer DOI 0712
BibRef

Liu, C.Y.[Chong-Yu], Yao, R.[Rui], Rezatofighi, S.H.[S. Hamid], Reid, I.D.[Ian D.], Shi, Q.F.[Qin-Feng],
Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference,
IP(29), No. 1, 2020, pp. 277-288.
IEEE DOI 1910
BibRef
Earlier:
Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference,
DICTA17(1-8)
IEEE DOI 1804
inference mechanisms, learning (artificial intelligence), motion estimation, neural nets, object recognition, graphical models. object detection, object tracking, optimisation, tracking, Visualization BibRef

Yan, Q.S.[Qing-Sen], Li, L.S.[Lin-Sheng],
Kernel sparse tracking with compressive sensing,
IET-CV(8), No. 4, August 2014, pp. 305-315.
DOI Link 1407
Appearance changes. Use 2 stage sparse representation. BibRef

Bai, Y.C.[Yan-Cheng], Tang, M.[Ming],
Object Tracking via Robust Multitask Sparse Representation,
SPLetters(21), No. 8, August 2014, pp. 909-913.
IEEE DOI 1406
BibRef
Earlier:
Robust tracking via weakly supervised ranking SVM,
CVPR12(1854-1861).
IEEE DOI 1208
BibRef
Earlier:
Robust visual tracking via ranking SVM,
ICIP11(517-520).
IEEE DOI 1201
Joints BibRef

Bai, Y.C.[Yan-Cheng], Tang, M.[Ming],
Robust visual tracking via augmented kernel SVM,
IVC(32), No. 8, 2014, pp. 465-475.
Elsevier DOI 1407
Feature representation BibRef

Choi, H.S.[Hong Seok], Kim, I.S.[In Su], Choi, J.Y.[Jin Young],
Combining histogram-wise and pixel-wise matchings for kernel tracking through constrained optimization,
CVIU(118), No. 1, 2014, pp. 61-70.
Elsevier DOI 1312
Object tracking BibRef

Ma, C.[Chao], Liu, C.C.[Chuan-Cai],
Two dimensional hashing for visual tracking,
CVIU(135), No. 1, 2015, pp. 83-94.
Elsevier DOI 1504
Hashing BibRef

Qian, C., Xu, Z.,
Robust Visual Tracking via Sparse Representation Under Subclass Discriminant Constraint,
CirSysVideo(26), No. 7, July 2016, pp. 1293-1307.
IEEE DOI 1608
image coding BibRef

Zhang, L.[Le], Suganthan, P.N.[Ponnuthurai Nagaratnam],
Robust visual tracking via co-trained Kernelized correlation filters,
PR(69), No. 1, 2017, pp. 82-93.
Elsevier DOI 1706
Visual tracking BibRef

Zhang, L.[Le], Suganthan, P.N.[Ponnuthurai Nagaratnam],
Visual Tracking With Convolutional Random Vector Functional Link Network,
Cyber(47), No. 10, October 2017, pp. 3243-3253.
IEEE DOI 1709
Biological neural networks, Data models, Target tracking, Training, convolutional random vector functional link (CRVFL), deep learning, random vector functional link (RVFL), visual, tracking BibRef

Shi, Q.[Qiushi], Katuwal, R.[Rakesh], Suganthan, P.N., Tanveer, M.,
Random vector functional link neural network based ensemble deep learning,
PR(117), 2021, pp. 107978.
Elsevier DOI 2106
Random Vector Functional Link (RVFL), Deep RVFL, Multi-layer RVFL, Ensemble deep learning, Extreme learning machine (ELM) BibRef

Farahi, F.[Fahime], Yazdi, H.S.[Hadi Sadoghi],
Probabilistic Kalman filter for moving object tracking,
SP:IC(82), 2020, pp. 115751.
Elsevier DOI 2001
Kalman filter, Learned tracker, Smoothing process, Graph BibRef

Huang, B.[Bo], Xu, T.F.[Ting-Fa], Jiang, S.W.[Shen-Wang], Chen, Y.W.[Yi-Wen], Bai, Y.[Yu],
Robust Visual Tracking via Constrained Multi-Kernel Correlation Filters,
MultMed(22), No. 11, November 2020, pp. 2820-2832.
IEEE DOI 2010
Kernel, Correlation, Target tracking, Adaptation models, Training, Frequency-domain analysis, Feature extraction, adaptive updating BibRef


Sun, C., Wang, D., Lu, H., Yang, M.,
Learning Spatial-Aware Regressions for Visual Tracking,
CVPR18(8962-8970)
IEEE DOI 1812
Kernel, Target tracking, Neural networks, Correlation, Visualization, Training, Convolution BibRef

Kutschbach, T., Bochinski, E., Eiselein, V., Sikora, T.,
Sequential sensor fusion combining probability hypothesis density and kernelized correlation filters for multi-object tracking in video data,
AVSS17(1-5)
IEEE DOI 1806
Gaussian processes, filtering theory, object tracking, video signal processing, GMPHD tracking-by-detection scheme, Visualization BibRef

Bibi, A.[Adel], Mueller, M.[Matthias], Ghanem, B.[Bernard],
Target Response Adaptation for Correlation Filter Tracking,
ECCV16(VI: 419-433).
Springer DOI 1611
BibRef
Earlier: A1, A3, Only:
Multi-template Scale-Adaptive Kernelized Correlation Filters,
VOT15(613-620)
IEEE DOI 1602
Correlation Drawbacks of Kernelized Correlation Filter (KCF) tracker. Some alternatives. BibRef

Montero, A.S., Lang, J., Laganiere, R.,
Scalable Kernel Correlation Filter with Sparse Feature Integration,
VOT15(587-594)
IEEE DOI 1602
Computational modeling BibRef

Ge, T.[Ting], Lu, Y.[Yao],
Multiple kernel boosting based tracking using pooling features,
ICIP15(3210-3214)
IEEE DOI 1512
multiple kernel boosting; object tracking; pooling BibRef

Li, P.[Peng], Cai, Z.P.[Zhi-Peng], Wang, C.[Cheng], Sun, Z.[Zhuo], Wang, H.Y.[Han-Yun], Li, J.[Jonathan],
Cascade framework for object extraction in image sequences,
CVRS12(58-62).
IEEE DOI 1302
BibRef

Li, P.[Peng], Cai, Z.P.[Zhi-Peng], Wang, H.Y.[Han-Yun], Sun, Z.[Zhuo], Yi, Y.[Yunhui], Wang, C.[Cheng], Li, J.[Jonathan],
Scale invariant kernel-based object tracking,
CVRS12(252-255).
IEEE DOI 1302
BibRef

Zulkifley, M.A.[Mohd Asyraf], Moran, B.[Bill],
Statistical Patch-Based Observation for Single Object Tracking,
CIAP11(II: 119-129).
Springer DOI 1109
BibRef
And:
Getting Robust Observation for Single Object Tracking: A Statistical Kernel-Based Approach,
CAIP11(I: 351-359).
Springer DOI 1109
BibRef

Nguyen, Q.A.[Quang Anh], Robles-Kelly, A.[Antonio], Zhou, J.[Jun],
A Graph-Based Feature Combination Approach to Object Tracking,
ACCV09(II: 224-235).
Springer DOI 0909
BibRef

Majeed, I., Arif, O.,
Kernel Subspace Integral Image Based Probabilistic Visual Object Tracking,
DICTA15(1-7)
IEEE DOI 1603
estimation theory BibRef

Arif, O.[Omar], Vela, P.A.[Patricio Antonio],
Non-rigid Object Localization and Segmentation Using Eigenspace Representation,
ICCV09(803-808).
IEEE DOI 0909
BibRef

Arif, O.[Omar], Vela, P.A.[Patricio Antonio],
Kernel map compression using generalized radial basis functions,
ICCV09(1119-1124).
IEEE DOI 0909
BibRef
And:
Kernel covariance image region description for object tracking,
ICIP09(865-868).
IEEE DOI 0911
BibRef
And:
Robust Density Comparison for Visual Tracking,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Ma, L.[Lili], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Multi-cue collaborative kernel tracking with cross ratio invariant constraint,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Megret, R.[Rémi], Mikram, M.[Mounia], Berthoumieu, Y.[Yannick],
Inverse Composition for Multi-kernel Tracking,
ICCVGIP06(480-491).
Springer DOI 0612
BibRef

Tyagi, A.[Ambrish], Davis, J.W.[James W.],
A Context-Based Tracker Switching Framework,
Motion08(1-8).
IEEE DOI 0801
BibRef

Tyagi, A.[Ambrish], Davis, J.W.[James W.], Potamianos, G.[Gerasimos],
Steepest Descent For Efficient Covariance Tracking,
Motion08(1-6).
IEEE DOI 0801
BibRef

Tyagi, A.[Ambrish], Keck, Jr., M.A.[Mark A.], Davis, J.W.[James W.], Potamianos, G.[Gerasimos],
Kernel-Based 3D Tracking,
VS07(1-8).
IEEE DOI 0706
BibRef

Tyagi, A.[Ambrish], Potamianos, G.[Gerasimos], Davis, J.W.[James W.], Chu, S.M.[Stephen M.],
Fusion of Multiple Camera Views for Kernel-Based 3D Tracking,
Motion07(1-1).
IEEE DOI 0702
BibRef

Dewan, M.[Maneesh],
Toward Optimal Kernel-based Tracking,
CVPR06(I: 618-625).
IEEE DOI 0606
BibRef

Hager, G.D., Dewan, M., Stewart, C.V.,
Multiple kernel tracking with SSD,
CVPR04(I: 790-797).
IEEE DOI 0408
More efficient matching.
See also Kernel Smoothing. BibRef

Peng, N.S.[Ning-Song], Yang, J.[Jie], Chen, J.X.[Jia-Xin],
Kernel-Bandwidth Adaptation for Tracking Object Changing in Size,
ICIAR04(II: 581-588).
Springer DOI 0409
BibRef

Liu, Z.[Zhi], Shen, L.Q.[Li-Quan], Han, Z.M.[Zhong-Min], Zhang, Z.Y.[Zhao-Yang],
A Novel Video Object Tracking Approach Based on Kernel Density Estimation and Markov Random Field,
ICIP07(III: 373-376).
IEEE DOI 0709
BibRef

Nguyen, Q.A.[Quang Anh], Robles-Kelly, A.[Antonio], Shen, C.H.[Chun-Hua],
Kernel-based Tracking from a Probabilistic Viewpoint,
CVPR07(1-8).
IEEE DOI 0706
BibRef
Earlier:
Enhanced Kernel-Based Tracking for Monochromatic and Thermographic Video,
AVSBS06(28-28).
IEEE DOI 0611
BibRef

Parameswaran, V.[Vasu], Ramesh, V.[Visvanathan], Zoghlami, I.[Imad],
Tunable Kernels for Tracking,
CVPR06(II: 2179-2186).
IEEE DOI 0606
BibRef

Namboodiri, V.P.[Vinay P.], Ghorawat, A.[Amit], Chaudhuri, S.[Subhasis],
Improved Kernel-Based Object Tracking Under Occluded Scenarios,
ICCVGIP06(504-515).
Springer DOI 0612
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Target Tracking Techniques, Kalman Filters .


Last update:Sep 12, 2021 at 22:38:33