16.6.2.4.1 Prediction for Tracking Techniques

Chapter Contents (Back)
Motion Prediction. Motion Model. Prediction.
See also Target Tracking Techniques, Prediction, Trajectory Based.

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Elsevier DOI 1610
Visual object tracking BibRef

Gan, W.H.[Wei-Hao], Lee, M.S.[Ming-Sui], Wu, C.H.[Chi-Hao], Kuo, C.C.J.[C.C. Jay],
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JVCIR(53), 2018, pp. 180-191.
Elsevier DOI 1805
Object tracking, Online tracking, Convolutional neural network, Optical flow, Multi-domain learning BibRef

Huang, S.Y.[Si-Yu], Li, X.[Xi], Zhang, Z.F.[Zhong-Fei], He, Z.Z.[Zhou-Zhou], Wu, F.[Fei], Liu, W.[Wei], Tang, J.H.[Jin-Hui], Zhuang, Y.T.[Yue-Ting],
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feature extraction BibRef

Liang, N., Wu, G., Kang, W., Wang, Z., Feng, D.D.,
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MultMed(20), No. 9, September 2018, pp. 2289-2302.
IEEE DOI 1809
computational complexity, image motion analysis, learning (artificial intelligence), object detection, dual SVMs BibRef

Wang, J.Z.[Jin-Zhuo], Wang, W.M.[Wen-Min], Gao, W.[Wen],
Predicting Diverse Future Frames With Local Transformation-Guided Masking,
CirSysVideo(29), No. 12, December 2019, pp. 3531-3543.
IEEE DOI 1912
Predictive models, Generators, Task analysis, Visualization, Computational modeling, Complexity theory, Training, video prediction on single frame BibRef

Chen, R.[Rui], Chen, M.J.[Ming-Jian], Li, W.L.[Wan-Li], Guo, N.K.[Nai-Kun],
Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network,
IJGI(9), No. 2, 2020, pp. xx-yy.
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Zhang, K.[Kunpeng], Feng, X.L.[Xiao-Liang], Wu, L.[Lan], He, Z.B.[Zheng-Bing],
Trajectory Prediction for Autonomous Driving Using Spatial-Temporal Graph Attention Transformer,
ITS(23), No. 11, November 2022, pp. 22343-22353.
IEEE DOI 2212
Trajectory, Roads, Predictive models, Transformers, Feature extraction, Geometry, Autonomous vehicles, spatial-temporal interaction BibRef

Hu, H.N.[Hou-Ning], Yang, Y.H.[Yung-Hsu], Fischer, T.[Tobias], Darrell, T.J.[Trevor J.], Yu, F.[Fisher], Sun, M.[Min],
Monocular Quasi-Dense 3D Object Tracking,
PAMI(45), No. 2, February 2023, pp. 1992-2008.
IEEE DOI 2301
Object detection, Object tracking, Benchmark testing, Trajectory, Autonomous vehicles, Target tracking, Monocular 3D detection, quasi-dense similarity learning BibRef

Gao, H.[Hang], Xu, H.Z.[Hua-Zhe], Cai, Q.Z.[Qi-Zhi], Wang, R.[Ruth], Yu, F.[Fisher], Darrell, T.J.[Trevor J.],
Disentangling Propagation and Generation for Video Prediction,
ICCV19(9005-9014)
IEEE DOI 2004
image colour analysis, image motion analysis, image resolution, image sequences, learning (artificial intelligence), neural nets, Standards BibRef


Villar-Corrales, A.[Angel], Wahdan, I.[Ismail], Behnke, S.[Sven],
Object-Centric Video Prediction Via Decoupling of Object Dynamics and Interactions,
ICIP23(570-574)
IEEE DOI 2312
BibRef

Wang, X.[Xishun], Su, T.[Tong], Da, F.[Fang], Yang, X.D.[Xiao-Dong],
ProphNet: Efficient Agent-Centric Motion Forceasting with Anchor-Informed Proposals,
CVPR23(21995-22003)
IEEE DOI 2309
BibRef

Liu, H.[Huan], Chi, Z.X.[Zhi-Xiang], Yu, Y.H.[Yuan-Hao], Wang, Y.[Yang], Chen, J.[Jun], Tang, J.[Jin],
Meta-Auxiliary Learning for Future Depth Prediction in Videos,
WACV23(5745-5754)
IEEE DOI 2302
Adaptation models, Decision making, Estimation, Predictive models, Task analysis, Intelligent systems, Image reconstruction BibRef

Nawhal, M.[Megha], Jyothi, A.A.[Akash Abdu], Mori, G.[Greg],
Rethinking Learning Approaches for Long-Term Action Anticipation,
ECCV22(XXXIV:558-576).
Springer DOI 2211
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Epstein, D.[Dave], Wu, J.J.[Jia-Jun], Schmid, C.[Cordelia], Sun, C.[Chen],
Learning Temporal Dynamics from Cycles in Narrated Video,
ICCV21(1460-1469)
IEEE DOI 2203
Training, Visualization, Computational modeling, Predictive models, Data models, Video analysis and understanding BibRef

Tanke, J.[Julian], Zhang, L.G.[Lin-Guang], Zhao, A.[Amy], Tang, C.C.[Cheng-Cheng], Cai, Y.J.[Yu-Jun], Wang, L.[Lezi], Wu, P.C.[Po-Chen], Gall, J.[Juergen], Keskin, C.[Cem],
Social Diffusion: Long-term Multiple Human Motion Anticipation,
ICCV23(9567-9577)
IEEE DOI 2401
BibRef

Tanke, J.[Julian], Zaveri, C.[Chintan], Gall, J.[Juergen],
Intention-based Long-Term Human Motion Anticipation,
3DV21(596-605)
IEEE DOI 2201
Measurement, Uncertainty, Forecasting, pose forecasting, human motion BibRef

Behrmann, N.[Nadine], Gall, J.[Juergen], Noroozi, M.[Mehdi],
Unsupervised Video Representation Learning by Bidirectional Feature Prediction,
WACV21(1669-1678)
IEEE DOI 2106
Prediction methods, Encoding, Task analysis BibRef

Liu, Y.[Yuan], Li, R.[Ruoteng], Cheng, Y.[Yu], Tan, R.T.[Robby T.], Sui, X.[Xiubao],
Object Tracking Using Spatio-temporal Networks for Future Prediction Location,
ECCV20(XXII:1-17).
Springer DOI 2011
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Šaric, J., Oršic, M., Antunovic, T., Vražic, S., Šegvic, S.,
Warp to the Future: Joint Forecasting of Features and Feature Motion,
CVPR20(10645-10654)
IEEE DOI 2008
Forecasting, Semantics, Adaptive optics, Optical imaging, Predictive models, Correlation, Casting BibRef

Rempe, D.[Davis], Sridhar, S.[Srinath], Wang, H.[He], Guibas, L.J.[Leonidas J.],
Predicting the Physical Dynamics of Unseen 3D Objects,
WACV20(2823-2832)
IEEE DOI 2006
Shape, Dynamics, Friction, Angular velocity, Robots BibRef

Ho, Y., Cho, C., Jin, G., Peng, W.,
SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement Learning,
ICCV19(10461-10469)
IEEE DOI 2004
data compression, image sequences, learning (artificial intelligence), motion compensation, BibRef

Hu, Z., Wang, J.,
A Novel Adversarial Inference Framework for Video Prediction with Action Control,
SDL-CV19(768-772)
IEEE DOI 2004
image motion analysis, image representation, image segmentation, image sequences, neural nets, Cycle Consistent BibRef

Hoyer, L., Kesper, P., Khoreva, A., Fischer, V.,
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids,
CVRSUAD19(813-821)
IEEE DOI 2004
cameras, decision making, image fusion, image segmentation, image sequences, motion estimation, environment representation BibRef

Sadeghian, A.[Amir], Kosaraju, V.[Vineet], Sadeghian, A.[Ali], Hirose, N.[Noriaki], Rezatofighi, H.[Hamid], Savarese, S.[Silvio],
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints,
CVPR19(1349-1358).
IEEE DOI 2002
BibRef

Zhu, D.[Deyao], Munderloh, M.[Marco], Rosenhahn, B.[Bodo], Stückler, J.[Jörg],
Learning to Disentangle Latent Physical Factors for Video Prediction,
GCPR19(595-608).
Springer DOI 1911
BibRef

Baik, S., Kwon, J., Lee, K.M.,
Learning to Remember Past to Predict Future for Visual Tracking,
ICIP19(3068-3072)
IEEE DOI 1910
tracking, neural network BibRef

Singh, G.[Gurkirt], Saha, S.[Suman], Cuzzolin, F.[Fabio],
Predicting Action Tubes,
AnticipateBeh18(III:106-123).
Springer DOI 1905
BibRef

Terwilliger, A.[Adam], Brazil, G.[Garrick], Liu, X.M.[Xiao-Ming],
Recurrent Flow-Guided Semantic Forecasting,
WACV19(1703-1712)
IEEE DOI 1904
Predict future motion. image motion analysis, image segmentation, image sequences, learning (artificial intelligence), Predictive models BibRef

Vukotic, V.[Vedran], Pintea, S.L.[Silvia-Laura], Raymond, C.[Christian], Gravier, G.[Guillaume], van Gemert, J.C.[Jan C.],
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network,
CIAP17(I:140-151).
Springer DOI 1711
BibRef

Vondrick, C.[Carl], Torralba, A.B.[Antonio B.],
Generating the Future with Adversarial Transformers,
CVPR17(2992-3000)
IEEE DOI 1711
Generators, Network architecture, Predictive models, Robots, Semantics, Spatial resolution, Visualization BibRef

Zhou, Y.P.[Yi-Pin], Berg, T.L.[Tamara L.],
Learning Temporal Transformations from Time-Lapse Videos,
ECCV16(VIII: 262-277).
Springer DOI 1611
Predict the changes like people do. BibRef

Mottaghi, R.[Roozbeh], Bagherinezhad, H., Rastegari, M.[Mohammad], Farhadi, A.[Ali],
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images,
CVPR16(3521-3529)
IEEE DOI 1612
BibRef

Mottaghi, R.[Roozbeh], Rastegari, M.[Mohammad], Gupta, A.[Abhinav], Farhadi, A.[Ali],
'What Happens If...' Learning to Predict the Effect of Forces in Images,
ECCV16(IV: 269-285).
Springer DOI 1611
BibRef

Ballan, L.[Lamberto], Castaldo, F.[Francesco], Alahi, A.[Alexandre], Palmieri, F.[Francesco], Savarese, S.[Silvio],
Knowledge Transfer for Scene-Specific Motion Prediction,
ECCV16(I: 697-713).
Springer DOI 1611
BibRef

Rodriguez, C.[Cristian], Fernando, B.[Basura], Li, H.D.[Hong-Dong],
Action Anticipation by Predicting Future Dynamic Images,
AnticipateBeh18(III:89-105).
Springer DOI 1905
BibRef

Li, Z.C.[Zhi-Cheng], Qiao, B.[Bing], Deng, S.B.[Shao-Bin],
Color-Based Visual Object Tracking with Prediction and Error Judgment,
CISP09(1-4).
IEEE DOI 0910
BibRef

Rajpurohit, V.S.[Vijay S.], Pai, M.M.M.[M. M. Manohara],
An Optimized Fuzzy Based Short Term Object Motion Prediction for Real-Life Robot Navigation Environment,
Visual08(xx-yy).
Springer DOI 0809
BibRef

Gupta, N., Mittal, P., Patwardhan, K.S., Roy, S.D., Chaudhury, S., Banerjee, S.,
On line predictive appearance-based tracking,
ICIP04(II: 1041-1044).
IEEE DOI 0505
BibRef

Zhou, K.[Kun], Dai, Q.H.[Qiong-Hai], Wu, J.[Jiang], Er, G.H.[Gui-Hua],
Fast tracking of semantic video object based on motion prediction and subregion extraction,
ICIP02(III: 621-624).
IEEE DOI 0210
BibRef

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


Last update:Apr 18, 2024 at 11:38:49