16.6.2.2 Tracking using Neural Nets, Learning

Anguita, D., Parodi, G., Zunino, R.,
Neural Structures for Visual-Motion Tracking,
MVA(8), No. 5, 1995, pp. 275-288.
Springer DOI BibRef 9500

Agarwal, A.[Abhinav], Datla, S.[Sanketh], Tyagi, B.[Barjeev], Niyogi, R.[Rajdeep],
Novel design for real time path tracking with computer vision using neural networks,
IJCVR(1), No. 4, 2010, pp. 380-391.
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], Raju, P.M.[Priya Mariam], Mishra, D.[Deepak], Subrahmanyam, G.R.K.S.[Gorthi Rama Krishna Sai],
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking,
ACCV18(II:646-661).
Springer DOI 1906
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

Tang, F.[Fuhui], Lu, X.K.[Xian-Kai], Zhang, X.Y.[Xiao-Yu], Luo, L.K.[Ling-Kun], Hu, S.Q.[Shi-Qiang], Zhang, H.L.[Huan-Long],
Adaptive convolutional layer selection based on historical retrospect for visual tracking,
IET-CV(13), No. 3, April 2019, pp. 345-353.
DOI Link 1904
BibRef

Li, N.[Ning], Ji, Q.G.[Qing-Ge], Ma, T.J.[Tian-Jun],
ACT: an ACTNet for visual tracking,
IET-IPR(13), No. 5, 18 April 2019, pp. 722-728.
DOI Link 1904
BibRef

Dong, X.P.[Xing-Ping], Shen, J.B.[Jian-Bing], Wu, D.M.[Dong-Ming], Guo, K.[Kan], Jin, X.G.[Xiao-Gang], Porikli, F.M.[Fatih M.],
Quadruplet Network With One-Shot Learning for Fast Visual Object Tracking,
IP(28), No. 7, July 2019, pp. 3516-3527.
IEEE DOI 1906
image representation, learning (artificial intelligence), object tracking, one-shot learning, fast visual object tracking, Siamese deep network BibRef

Zhao, F., Wang, J., Wu, Y., Tang, M.,
Adversarial Deep Tracking,
CirSysVideo(29), No. 7, July 2019, pp. 1998-2011.
IEEE DOI 1907
Target tracking, Visualization, Task analysis, Training, Computational modeling, Neural networks, Correlation, attention BibRef

Zhai, M.Y.[Meng-Yao], Chen, L.[Lei], Mori, G.[Greg], Roshtkhari, M.J.[Mehrsan Javan],
Deep Learning of Appearance Models for Online Object Tracking,
WiCV-E18(IV:681-686).
Springer DOI 1905
BibRef

Jung, I.[Ilchae], Son, J.[Jeany], Baek, M.[Mooyeol], Han, B.H.[Bo-Hyung],
Real-Time MDNet,
ECCV18(II: 89-104).
Springer DOI 1810
multi-domain convolutional neural network for tracking. 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 .