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Zunino, R.,
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MVA(8), No. 5, 1995, pp. 275-288.
Springer DOI
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9500
Agarwal, A.[Abhinav],
Datla, S.[Sanketh],
Tyagi, B.[Barjeev],
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Novel design for real time path tracking with computer vision using
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DOI Link
1102
BibRef
Mei, X.[Xue],
Ling, H.B.[Hai-Bin],
Robust Visual Tracking and Vehicle Classification via Sparse
Representation,
PAMI(33), No. 11, November 2011, pp. 2259-2272.
IEEE DOI
1110
BibRef
Earlier:
Robust Visual Tracking Using L1 Minimization,
ICCV09(1436-1443).
IEEE DOI
PDF File.
0909
See also Illumination Recovery From Image With Cast Shadows Via Sparse Representation.
BibRef
Fan, H.[Heng],
Ling, H.B.[Hai-Bin],
Parallel Tracking and Verifying: A Framework for Real-Time and High
Accuracy Visual Tracking,
ICCV17(5487-5495)
IEEE DOI
1802
BibRef
And:
SANet: Structure-Aware Network for Visual Tracking,
DeepLearn-T17(2217-2224)
IEEE DOI
1709
SLAM (robots), learning (artificial intelligence),
multi-threading, object tracking, parallel processing,
Visualization.
Computational modeling, Feature extraction,
Recurrent neural networks, Robustness, Target tracking.
BibRef
Li, H.X.[Han-Xi],
Li, Y.[Yi],
Porikli, F.M.[Fatih M.],
DeepTrack: Learning Discriminative Feature Representations Online for
Robust Visual Tracking,
IP(25), No. 4, April 2016, pp. 1834-1848.
IEEE DOI
1604
BibRef
Earlier:
DeepTrack: Learning Discriminative Feature Representations by
Convolutional Neural Networks for Visual Tracking,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Li, H.X.[Han-Xi],
Li, Y.[Yi],
Porikli, F.M.[Fatih M.],
Convolutional neural net bagging for online visual tracking,
CVIU(153), No. 1, 2016, pp. 120-129.
Elsevier DOI
1612
Visual tracking
BibRef
Earlier:
Robust Online Visual Tracking with a Single Convolutional Neural
Network,
ACCV14(V: 194-209).
Springer DOI
1504
Feature extraction
BibRef
Hu, H.,
Ma, B.,
Shen, J.,
Sun, H.,
Shao, L.,
Porikli, F.M.[Fatih M.],
Robust Object Tracking Using Manifold Regularized Convolutional
Neural Networks,
MultMed(21), No. 2, February 2019, pp. 510-521.
IEEE DOI
1902
Visualization, Manifolds, Target tracking, Laplace equations,
Training, Feature extraction, Convolutional neural networks,
online tracking
BibRef
Zhu, H.[Hao],
Porikli, F.M.[Fatih M.],
Automatic Refinement Strategies for Manual Initialization of Object
Trackers,
IP(26), No. 2, February 2017, pp. 821-835.
IEEE DOI
1702
computer vision
BibRef
Zhu, G.[Gao],
Porikli, F.M.[Fatih M.],
Li, H.D.[Hong-Dong],
Not All Negatives Are Equal:
Learning to Track With Multiple Background Clusters,
CirSysVideo(28), No. 2, February 2018, pp. 314-326.
IEEE DOI
1802
BibRef
Earlier:
Model-Free Multiple Object Tracking with Shared Proposals,
ACCV16(II: 288-304).
Springer DOI
1704
BibRef
And:
Beyond Local Search:
Tracking Objects Everywhere with Instance-Specific Proposals,
CVPR16(943-951)
IEEE DOI
1612
BibRef
And:
Robust Visual Tracking with Deep Convolutional Neural Network Based
Object Proposals on PETS,
PETS16(1265-1272)
IEEE DOI
1612
Context, Object tracking, Support vector machines, Training,
Visualization, Contextual clustering, fine-grained model,
tracking by detection
BibRef
Hussein, M.[Mohamed],
Porikli, F.M.[Fatih M.],
Li, R.[Rui],
Arslan, S.[Suayb],
CrossTrack: Robust 3D tracking from two cross-sectional views,
CVPR11(1041-1048).
IEEE DOI
1106
BibRef
Ma, B.,
Huang, L.,
Shen, J.,
Shao, L.,
Yang, M.H.,
Porikli, F.M.,
Visual Tracking Under Motion Blur,
IP(25), No. 12, December 2016, pp. 5867-5876.
IEEE DOI
1612
image motion analysis
BibRef
Huang, L.,
Ma, B.,
Shen, J.,
He, H.,
Shao, L.,
Porikli, F.M.[Fatih M.],
Visual Tracking by Sampling in Part Space,
IP(26), No. 12, December 2017, pp. 5800-5810.
IEEE DOI
1710
image sampling, object tracking,
probability sampling,
Part space,
BibRef
Ma, B.,
Hu, H.,
Shen, J.,
Zhang, Y.,
Porikli, F.M.[Fatih M.],
Linearization to Nonlinear Learning for Visual Tracking,
ICCV15(4400-4407)
IEEE DOI
1602
Dictionaries
BibRef
Guo, J.,
Xu, T.,
Deep Ensemble Tracking,
SPLetters(24), No. 10, October 2017, pp. 1562-1566.
IEEE DOI
1710
convolution, feature extraction, feedforward neural nets,
distance metric, template matching problem.
BibRef
Cao, Y.[Yi],
Ji, H.B.[Hong-Bing],
Zhang, W.[Wenbo],
Xue, F.[Fei],
Learning spatio-temporal context via hierarchical features for visual
tracking,
SP:IC(66), 2018, pp. 50-65.
Elsevier DOI
1806
Visual tracking, Convolutional neural network,
Transfer learning, Spatio-temporal context,
Training confidence index
BibRef
Zhu, Z.G.[Zhi-Gang],
Ji, H.B.[Hong-Bing],
Zhang, W.[Wenbo],
Huang, G.P.[Guo-Peng],
Temporal stochastic linear encoding networks,
SP:IC(70), 2019, pp. 14-20.
Elsevier DOI
1812
Convolutional neural network, Action recognition,
Long-range temporal dynamics, Motion boundary
BibRef
Hu, X.P.[Xiao-Peng],
Li, J.T.[Jing-Ting],
Yang, Y.[Yan],
Wang, F.[Fan],
Reliability verification-based convolutional neural networks for object
tracking,
IET-IPR(13), No. 1, January 2019, pp. 175-185.
DOI Link
1812
BibRef
Wang, T.[Tao],
Ling, H.B.[Hai-Bin],
Gracker: A Graph-Based Planar Object Tracker,
PAMI(40), No. 6, June 2018, pp. 1494-1501.
IEEE DOI
1805
Algorithm design and analysis, Benchmark testing,
Object tracking, Robustness, Target tracking, Visualization,
pose estimation
BibRef
Rout, L.[Litu],
Siddhartha,
Mishra, D.[Deepak],
Subrahmanyam, G.R.K.S.[Gorthi Rama Krishna Sai],
Rotation Adaptive Visual Object Tracking with Motion Consistency,
WACV18(1047-1055)
IEEE DOI
1806
convolution, feature selection, feedforward neural nets,
image capture, image motion analysis, image sequences,
Visualization
BibRef
Liu, J.[Jun],
Shahroudy, A.[Amir],
Xu, D.[Dong],
Kot, A.C.,
Wang, G.[Gang],
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network
with Trust Gates,
PAMI(40), No. 12, December 2018, pp. 3007-3021.
IEEE DOI
1811
BibRef
Earlier: A1, A2, A3, A5, Only:
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition,
ECCV16(III: 816-833).
Springer DOI
1611
Recurrent neural networks, Spatiotemporal phenomena,
Feature extraction,
skeleton sequence
BibRef
Wang, L.[Li],
Pham, N.T.[Nam Trung],
Ng, T.T.[Tian-Tsong],
Wang, G.[Gang],
Chan, K.L.[Kap Luk],
Leman, K.[Karianto],
Learning Deep Features for Multiple Object Tracking by Using a
Multi-Task Learning Strategy,
ICIP14(838-842)
IEEE DOI
1502
Adaptation models
BibRef
Li, Y.D.[Yun-Dong],
Zhang, X.[Xueyan],
Li, H.G.[Hong-Guang],
Zhou, Q.C.[Qi-Chen],
Cao, X.B.[Xian-Bin],
Xiao, Z.F.[Zhi-Feng],
Object detection and tracking under Complex environment using deep
learning-based LPM,
IET-CV(13), No. 2, March 2019, pp. 157-164.
DOI Link
1902
BibRef
Zhou, H.Z.[Hui-Zhong],
Ummenhofer, B.[Benjamin],
Brox, T.[Thomas],
DeepTAM: Deep Tracking and Mapping,
ECCV18(XVI: 851-868).
Springer DOI
1810
BibRef
Wang, G.,
Liu, B.,
Li, W.,
Yu, N.,
Flow Guided Siamese Network for Visual Tracking,
ICIP18(231-235)
IEEE DOI
1809
Target tracking, Visualization, Optical imaging, Benchmark testing,
Prediction algorithms, Optical propagation, Siamese-network,
deep learning
BibRef
Kokul, T.,
Fookes, C.,
Sridharan, S.,
Ramanan, A.,
Pinidiyaarachchi, U.A.J.,
Gate connected convolutional neural network for object tracking,
ICIP17(2602-2606)
IEEE DOI
1803
Adaptation models, Logic gates, Machine learning,
Network architecture, Object tracking, Target tracking,
object tracking
BibRef
Teng, Z.[Zhu],
Xing, J.L.[Jun-Liang],
Wang, Q.[Qiang],
Lang, C.Y.[Cong-Yan],
Feng, S.H.[Song-He],
Jin, Y.[Yi],
Robust Object Tracking Based on Temporal and Spatial Deep Networks,
ICCV17(1153-1162)
IEEE DOI
1802
feature extraction, learning (artificial intelligence),
neural nets, object detection, object tracking, Feature Net,
Visualization
BibRef
Chakrabory, A.[Anit],
Dutta, S.[Sayandip],
A Machine Learning Inspired Approach for Detection, Recognition and
Tracking of Moving Objects from Real-Time Video,
PReMI17(170-178).
Springer DOI
1711
BibRef
Song, Y.,
Ma, C.,
Gong, L.,
Zhang, J.,
Lau, R.W.H.,
Yang, M.H.,
CREST: Convolutional Residual Learning for Visual Tracking,
ICCV17(2574-2583)
IEEE DOI
1802
convolution, feature extraction,
learning (artificial intelligence), neural nets, object tracking,
Visualization
BibRef
Guo, Q.,
Feng, W.,
Zhou, C.,
Huang, R.,
Wan, L.,
Wang, S.,
Learning Dynamic Siamese Network for Visual Object Tracking,
ICCV17(1781-1789)
IEEE DOI
1802
feature extraction, image classification, image representation,
image sequences, learning (artificial intelligence),
Visualization
BibRef
Serratosa, F.[Francesc],
Amézquita Gómez, N.[Nicolás],
Alquézar, R.[René],
Combining Neural Networks and Clustering Techniques for Object
Recognition in Indoor Video Sequences,
CIARP06(399-405).
Springer DOI
0611
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Target Tracking Techniques, Motion Model, Prediction, Control .