16.6.2.7.2 Multi-Object Tracking, Neural Networks, Learning

Leung, H.,
Neural-Network Data Association with Application to Multiple-Target Tracking,
OptEng(35), No. 3, March 1996, pp. 693-700. BibRef 9603

García, J.[Jesús], Molina, J.M.[José M.], Besada, J.A.[Juan A.], Portillo, J.I.[Javier I.],
A Multitarget Tracking Video System Based on Fuzzy and Neuro-Fuzzy Techniques,
JASP(2005), No. 14, 2005, pp. 2341-2358.
WWW Link. 0603
BibRef

Humphreys, J.[James], Hunter, A.[Andrew],
Multiple object tracking using a neural cost function,
IVC(27), No. 4, 3 March 2009, pp. 417-424.
Elsevier DOI 0804
Surveillance; Tracking; Background differencing; Self-organizing maps; Neural networks BibRef

Chu, P., Fan, H., Tan, C.C., Ling, H.,
Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment,
WACV19(161-170)
IEEE DOI 1904
convolutional neural nets, learning (artificial intelligence), object detection, object tracking, target tracking, Optimization BibRef

Xiong, H.K.[Hong-Kai], Zheng, D.[Dayu], Zhu, Q., Wang, B., Zheng, Y.F.[Yuan F.],
A Structured Learning-Based Graph Matching Method for Tracking Dynamic Multiple Objects,
CirSysVideo(23), No. 3, March 2013, pp. 534-548.
IEEE DOI 1303
BibRef
Earlier: A2, A1, A5, Only:
A structured learning-based graph matching for dynamic multiple object tracking,
ICIP11(2333-2336).
IEEE DOI 1201
BibRef

Xie, C.J.[Cheng-Jun], Tan, J.Q.[Jie-Qing], Chen, P.[Peng], Zhang, J.[Jie], He, L.[Lei],
Collaborative object tracking model with local sparse representation,
JVCIR(25), No. 2, 2014, pp. 423-434.
Elsevier DOI 1402
Object tracking BibRef

Xie, C.J.[Cheng-Jun], Tan, J.Q.[Jie-Qing], Chen, P.[Peng], Zhang, J.[Jie], He, L.[Lei],
Multi-scale patch-based sparse appearance model for robust object tracking,
MVA(25), No. 7, October 2014, pp. 1859-1876.
WWW Link. 1410
BibRef

Xie, C.J.[Cheng-Jun], Tan, J.Q.[Jie-Qing], Chen, P.[Peng], Zhang, J.[Jie], He, L.[Lei],
Multiple instance learning tracking method with local sparse representation,
IET-CV(7), No. 5, October 2013, pp. 320-334.
DOI Link 1402
image representation BibRef

Lan, X.Y.[Xiang-Yuan], Ma, A.J.H.[Andy Jin-Hua], Yuen, P.C.[Pong Chi], Chellappa, R.,
Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking,
IP(24), No. 12, December 2015, pp. 5826-5841.
IEEE DOI 1512
BibRef
Earlier: A1, A2, A3, Only:
Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation,
CVPR14(1194-1201)
IEEE DOI 1409
feature extraction BibRef

Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan], Wang, D.[Dong],
Visual Tracking via Structure Constrained Grouping,
SPLetters(22), No. 7, July 2015, pp. 794-798.
IEEE DOI 1412
image representation BibRef

Wang, L.J.[Li-Jun], Ouyang, W.L.[Wan-Li], Wang, X.G.[Xiao-Gang], Lu, H.C.[Hu-Chuan],
STCT: Sequentially Training Convolutional Networks for Visual Tracking,
CVPR16(1373-1381)
IEEE DOI 1612
BibRef
Earlier:
Visual Tracking with Fully Convolutional Networks,
ICCV15(3119-3127)
IEEE DOI 1602
Feature extraction BibRef

Li, F.[Fu], Lu, H.C.[Hu-Chuan], Wang, D.[Dong],
Robust Visual Tracking with Dual Group Structure,
ACCV14(IV: 614-629).
Springer DOI 1504
BibRef

Zhang, S., Lan, X.Y.[Xiang-Yuan], Qi, Y., Yuen, P.C.[Pong Chi],
Robust Visual Tracking via Basis Matching,
CirSysVideo(27), No. 3, March 2017, pp. 421-430.
IEEE DOI 1703
Dictionaries BibRef

Lan, X.Y.[Xiang-Yuan], Zhang, S.G.[Shen-Gping], Yuen, P.C.[Pong C.], Chellappa, R.[Rama],
Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker,
IP(27), No. 4, April 2018, pp. 2022-2037.
IEEE DOI 1802
image representation, learning (artificial intelligence), object tracking, video signal processing, appearance modeling, sparse representation BibRef

Zhang, S., Qi, Y., Jiang, F., Lan, X.Y.[Xiang-Yuan], Yuen, P.C.[Pong C.], Zhou, H.,
Point-to-Set Distance Metric Learning on Deep Representations for Visual Tracking,
ITS(19), No. 1, January 2018, pp. 187-198.
IEEE DOI 1801
Feature extraction, Manifolds, Target tracking, Training, Visualization, Metric learning, point to set, visual tracking BibRef

Wang, S.F.[Shao-Fei], Fowlkes, C.C.[Charless C.],
Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions,
IJCV(122), No. 3, May 2017, pp. 484-501.
Springer DOI 1704
BibRef
Earlier:
Learning Optimal Parameters For Multi-target Tracking,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Gwak, J.[Jeonghwan],
Multi-object tracking through learning relational appearance features and motion patterns,
CVIU(162), No. 1, 2017, pp. 103-115.
Elsevier DOI 1710
Multi-object tracking BibRef

Kang, B.[Bin], Zhu, W.P.[Wei-Ping], Liang, D.[Dong],
Robust multi-feature visual tracking via multi-task kernel-based sparse learning,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1172-1178.
DOI Link 1712
BibRef

Zhou, H., Ouyang, W., Cheng, J., Wang, X., Li, H.,
Deep Continuous Conditional Random Fields With Asymmetric Inter-Object Constraints for Online Multi-Object Tracking,
CirSysVideo(29), No. 4, April 2019, pp. 1011-1022.
IEEE DOI 1904
Tracking, Trajectory, Visualization, Neural networks, Machine learning, Mathematical model, Feature extraction, asymmetric pairwise terms BibRef

Chen, L., Ai, H., Chen, R., Zhuang, Z.,
Aggregate Tracklet Appearance Features for Multi-Object Tracking,
SPLetters(26), No. 11, November 2019, pp. 1613-1617.
IEEE DOI 1911
convolutional neural nets, feature extraction, image representation, object detection, object tracking, spatial-temporal attention BibRef

Kang, B.[Bin], Zhu, W.P.[Wei-Ping], Liang, D.[Dong], Chen, M.[Mingkai],
Robust visual tracking via nonlocal regularized multi-view sparse representation,
PR(88), 2019, pp. 75-89.
Elsevier DOI 1901
Sparse representation, Visual tracking, Multi-view learning, Dual group structure BibRef

Lan, L., Wang, X., Zhang, S., Tao, D., Gao, W., Huang, T.S.,
Interacting Tracklets for Multi-Object Tracking,
IP(27), No. 9, September 2018, pp. 4585-4597.
IEEE DOI 1807
Boolean functions, image motion analysis, learning (artificial intelligence), object detection, tracklets BibRef

Wang, J., Guo, Y., Tang, X., Hu, Q., An, W.,
Semi-Online Multiple Object Tracking Using Graphical Tracklet Association,
SPLetters(25), No. 11, November 2018, pp. 1725-1729.
IEEE DOI 1811
graph theory, learning (artificial intelligence), minimisation, object tracking, graphical tracklet association, tracklet association BibRef

Yang, T.[Tao], Cappelle, C.[Cindy], Ruichek, Y.[Yassine], El Bagdouri, M.[Mohammed],
Online multi-object tracking combining optical flow and compressive tracking in Markov decision process,
JVCIR(58), 2019, pp. 178-186.
Elsevier DOI 1901
BibRef
Earlier:
Multi-object Tracking Using Compressive Sensing Features in Markov Decision Process,
ACIVS17(505-517).
Springer DOI 1712
Multi-object tracking, Markov decision process, Tracking-learning-detection, Compressive sensing features BibRef

Makhura, O.J.[Onalenna J.], Woods, J.C.[John C.],
Learn-select-track: An approach to multi-object tracking,
SP:IC(74), 2019, pp. 153-161.
Elsevier DOI 1904
Multi-object tracking, Object colours, Density-based clustering, Low level local features BibRef

Liu, M.J.[Ming-Jie], Jin, C.B.[Cheng-Bin], Yang, B.[Bin], Cui, X.N.[Xue-Nan], Kim, H.[Hakil],
Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association,
IET-CV(13), No. 3, April 2019, pp. 312-318.
DOI Link 1904
BibRef

Mhalla, A.[Ala], Chateau, T.[Thierry], Ben Amara, N.E.[Najoua Essoukri],
Spatio-temporal object detection by deep learning: Video-interlacing to improve multi-object tracking,
IVC(88), 2019, pp. 120-131.
Elsevier DOI 1908
Multi-object tracking, Interlacing and inverse interlacing models, Specialization, Interlaced deep detector BibRef

Yu, H.Y.[Hong-Yang], Li, G.R.[Guo-Rong], Su, L.[Li], Zhong, B.N.[Bi-Neng], Yao, H.X.[Hong-Xun], Huang, Q.M.[Qing-Ming],
Conditional GAN based individual and global motion fusion for multiple object tracking in UAV videos,
PRL(131), 2020, pp. 219-226.
Elsevier DOI 2004
Multi-object tracking, Neural networks, UAV BibRef

Zhang, X.C.[Xing-Chen], Ye, P.[Ping], Peng, S.Y.[Sheng-Yun], Liu, J.[Jun], Xiao, G.[Gang],
DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion,
SP:IC(84), 2020, pp. 115756.
Elsevier DOI 2004
Object tracking, RGB-T fusion tracking, Dynamic Siamese networks, Deep learning, Image fusion BibRef

Li, P.X.[Pei-Xia], Chen, B.[Boyu], Wang, D.[Dong], Lu, H.C.[Hu-Chuan],
Visual tracking by dynamic matching-classification network switching,
PR(107), 2020, pp. 107419.
Elsevier DOI 2008
Visual Tracking, Deep Learning, Ensemble learning BibRef

Sharma, A.[Anil], Anand, S.[Saket], Kaul, S.K.[Sanjit K.],
Intelligent querying for target tracking in camera networks using deep Q-learning with n-step bootstrapping,
IVC(103), 2020, pp. 104022.
Elsevier DOI 2011
Camera networks, Deep reinforcement learning, Target tracking, Multi-camera tracking 2010 MSC: 00-01, 99-00 BibRef

Chuang, T.Y.[Tzu-Yi], Han, J.Y.[Jen-Yu], Jhan, D.J.[Deng-Jie], Yang, M.D.[Ming-Der],
Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Emami, P.[Patrick], Pardalos, P.M.[Panos M.], Elefteriadou, L.[Lily], Ranka, S.[Sanjay],
Machine Learning Methods for Data Association in Multi-Object Tracking,
Surveys(53), No. 4, August 2020, pp. xx-yy.
DOI Link 2010
deep learning, machine learning, Multi-object tracking, data association BibRef

Sun, S.J.[Shi-Jie], Akhtar, N.[Naveed], Song, H.S.[Huan-Sheng], Mian, A.[Ajmal], Shah, M.[Mubarak],
Deep Affinity Network for Multiple Object Tracking,
PAMI(43), No. 1, January 2021, pp. 104-119.
IEEE DOI 2012
Object tracking, Computational modeling, Deep learning, Detectors, Target tracking, Feature extraction, Multiple object tracking, on-line tracking BibRef

Ma, C.[Cong], Yang, F.[Fan], Li, Y.[Yuan], Jia, H.Z.[Hui-Zhu], Xie, X.D.[Xiao-Dong], Gao, W.[Wen],
Deep Human-Interaction and Association by Graph-Based Learning for Multiple Object Tracking in the Wild,
IJCV(129), No. 6, June 2021, pp. 1993-2010. 2106
BibRef

Ma, C.[Cong], Yang, F.[Fan], Li, Y.[Yuan], Jia, H.Z.[Hui-Zhu], Xie, X.D.[Xiao-Dong], Gao, W.[Wen],
Deep Trajectory Post-Processing and Position Projection for Single & Multiple Camera Multiple Object Tracking,
IJCV(129), No. 12, December 2021, pp. 3255-3278.
Springer DOI 2111
BibRef

Liu, Q.[Qiao], Li, X.[Xin], He, Z.Y.[Zhen-Yu], Fan, N.[Nana], Yuan, D.[Di], Wang, H.P.[Hong-Peng],
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking,
MultMed(23), 2021, pp. 2114-2126.
IEEE DOI 2107
Object tracking, Semantics, Training, Task analysis, Adaptation models, Correlation, Feature extraction, Thermal infrared dataset BibRef

Liu, Q.[Qiao], Yuan, D.[Di], Fan, N.[Nana], Gao, P.[Peng], Li, X.[Xin], He, Z.Y.[Zhen-Yu],
Learning Dual-Level Deep Representation for Thermal Infrared Tracking,
MultMed(25), 2023, pp. 1269-1281.
IEEE DOI 2305
Object tracking, Biological system modeling, Task analysis, Correlation, Multitasking, Adaptation models, Feature extraction, Thermal infrared dataset BibRef

Jiang, B.[Bo], Zhang, Y.[Yuan], Luo, B.[Bin], Cao, X.C.[Xiao-Chun], Tang, J.[Jin],
STGL: Spatial-Temporal Graph Representation and Learning for Visual Tracking,
MultMed(23), 2021, pp. 2162-2171.
IEEE DOI 2107
Target tracking, Computational modeling, Visualization, Noise measurement, Semisupervised learning, Shape, graph learning BibRef

Wan, X.Y.[Xing-Yu], Cao, J.[Jiakai], Zhou, S.P.[San-Ping], Wang, J.J.[Jin-Jun], Zheng, N.N.[Nan-Ning],
Tracking Beyond Detection: Learning a Global Response Map for End-to-End Multi-Object Tracking,
IP(30), 2021, pp. 8222-8235.
IEEE DOI 2110
Trajectory, Target tracking, Object detection, Measurement, Task analysis, Feature extraction, Data models, deep neural network BibRef

Tu, Z.Z.[Zheng-Zheng], Lin, C.[Chun], Zhao, W.[Wei], Li, C.L.[Cheng-Long], Tang, J.[Jin],
M5L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking,
IP(31), 2022, pp. 85-98.
IEEE DOI 2112
Measurement, Robustness, Feature extraction, Fuses, Collaboration, Visualization, Task analysis, Deep metric learning, RGBT tracking BibRef

Li, S.W.[Sheng-Wu], Zhang, X.[Xuande], Xiong, J.[Jing], Ning, C.J.[Chen-Jing], Zhang, M.[Mingke],
Learning spatial self-attention information for visual tracking,
IET-IPR(16), No. 1, 2022, pp. 49-60.
DOI Link 2112
BibRef

Chen, Z.Z.[Zhong-Ze], Li, J.[Jing], Wu, J.[Jia], Chang, J.[Jun], Xiao, Y.[Yafu], Wang, X.T.[Xiao-Ting],
Drift-Proof Tracking With Deep Reinforcement Learning,
MultMed(24), 2022, pp. 609-624.
IEEE DOI 2202
Target tracking, Reinforcement learning, Training, Robustness, Object tracking, Measurement, Real-time systems, Object tracking, drift problems BibRef

Li, X.J.[Xiao-Jing], Huang, L.[Lei], Wei, Z.Q.[Zhi-Qiang],
A Twofold Convolutional Regression Tracking Network With Temporal and Spatial Mechanism,
CirSysVideo(32), No. 3, March 2022, pp. 1537-1551.
IEEE DOI 2203
Target tracking, Feature extraction, Visualization, Training, Correlation, Task analysis, Semantics, Visual tracking, spatial and temporal mechanism BibRef

Brasó, G.[Guillem], Cetintas, O.[Orcun], Leal-Taixé, L.[Laura],
Multi-Object Tracking and Segmentation Via Neural Message Passing,
IJCV(130), No. 12, December 2022, pp. 3035-3053.
Springer DOI 2211
BibRef

Ye, L.L.[Liang-Ling], Li, W.[Weida], Zheng, L.X.[Li-Xin], Zeng, Y.Y.[Yuan-Yue],
Lightweight and Deep Appearance Embedding for Multiple Object Tracking,
IET-CV(16), No. 6, 2022, pp. 489-503.
DOI Link 2208
BibRef

Saada, M.[Mohamad], Kouppas, C.[Christos], Li, B.H.[Bai-Hua], Meng, Q.G.[Qing-Gang],
A multi-object tracker using dynamic Bayesian networks and a residual neural network based similarity estimator,
CVIU(225), 2022, pp. 103569.
Elsevier DOI 2212
Multi-object tracking, Dynamic Bayesian networks, Residual neural networks, YOLO V5, MOTChallenge BibRef

Wang, M.[Mianzhao], Shi, F.[Fan], Zhao, M.[Meng], Jia, C.[Chen], Tian, W.W.[Wei-Wei], He, T.[Tian], Fu, Y.[Yu], Cheng, X.[Xu],
An Online Multiobject Tracking Network for Autonomous Driving in Areas Facing Epidemic,
ITS(23), No. 12, December 2022, pp. 25191-25200.
IEEE DOI 2212
Feature extraction, Correlation, Target tracking, Strain, Epidemics, Aggregates, Detectors, Multi-object tracking, epidemic areas, re-ID embedding BibRef

Zheng, Y.J.[Ya-Jing], Yu, Z.F.[Zhao-Fei], Wang, S.[Song], Huang, T.J.[Tie-Jun],
Spike-Based Motion Estimation for Object Tracking Through Bio-Inspired Unsupervised Learning,
IP(32), 2023, pp. 335-349.
IEEE DOI 2301
Cameras, Tracking, Neuromorphics, Vision sensors, Neurons, Motion estimation, Target tracking, Neuromorphic vision sensor, high-speed object tracking BibRef

Li, R.[Rui], Zhang, B.[Baopeng], Liu, J.[Jun], Liu, W.[Wei], Teng, Z.[Zhu],
Inference-Domain Network Evolution: A New Perspective for One-Shot Multi-Object Tracking,
IP(32), 2023, pp. 2147-2159.
IEEE DOI 2304
Task analysis, Feature extraction, Noise measurement, Cameras, Adaptation models, Annotations, data association BibRef

Nalaie, K.[Keivan], Zheng, R.[Rong],
AttTrack: Online Deep Attention Transfer for Multi-object Tracking,
WACV23(1654-1663)
IEEE DOI 2302
Training, Representation learning, Knowledge engineering, Degradation, Visual analytics, Surveillance, Object detection BibRef

Chen, X.[Xi], Zhang, Y.F.[Yi-Feng],
Multi-Object Tracking Based on Deep Path Aggregation Network,
ICIVC22(214-221)
IEEE DOI 2301
Location awareness, Target tracking, Object detection, Feature extraction, Real-time systems, Object tracking, real-time BibRef

Zhao, S.Y.[Shuang-Ye], Wu, Y.[Yubin], Wang, S.[Shuai], Ke, W.[Wei], Sheng, H.[Hao],
Mask Guided Spatial-Temporal Fusion Network for Multiple Object Tracking,
ICIP22(3231-3235)
IEEE DOI 2211
Target tracking, Neural networks, Feature extraction, Reliability, Object tracking, Multi-object tracking, tracking by detection, mask guided network BibRef

Pi, Z.X.[Zhi-Xiong], Wan, W.T.[Wei-Tao], Sun, C.[Chong], Gao, C.X.[Chang-Xin], Sang, N.[Nong], Li, C.[Chen],
Hierarchical Feature Embedding for Visual Tracking,
ECCV22(XXII:428-445).
Springer DOI 2211

WWW Link. BibRef

Song, L.C.[Liang-Chen], Gong, X.[Xuan], Planche, B.[Benjamin], Zheng, M.[Meng], Doermann, D.[David], Yuan, J.S.[Jun-Song], Chen, T.[Terrence], Wu, Z.Y.[Zi-Yan],
PREF: Predictability Regularized Neural Motion Fields,
ECCV22(XXII:664-681).
Springer DOI 2211
BibRef

Yu, S.Z.[Shu-Zhi], Wu, G.H.[Guan-Hang], Gu, C.H.[Chun-Hui], Fathy, M.E.[Mohammed E.],
TDT: Teaching Detectors to Track without Fully Annotated Videos,
L3D-IVU22(3939-3949)
IEEE DOI 2210
Training, Annotations, Detectors, Predictive models, Benchmark testing BibRef

He, J.W.[Jia-Wei], Huang, Z.[Zehao], Wang, N.Y.[Nai-Yan], Zhang, Z.X.[Zhao-Xiang],
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking,
CVPR21(5295-5305)
IEEE DOI 2111
Training, Deep learning, Image edge detection, Neural networks, Feature extraction, Object tracking, Quadratic programming BibRef

Wu, J.L.[Jia-Lian], Cao, J.L.[Jia-Le], Song, L.C.[Liang-Chen], Wang, Y.[Yu], Yang, M.[Ming], Yuan, J.S.[Jun-Song],
Track to Detect and Segment: An Online Multi-Object Tracker,
CVPR21(12347-12356)
IEEE DOI 2111
Solid modeling, Costs, Tracking, Motion segmentation, Neural networks, Object detection BibRef

Zhang, Z.P.[Zhi-Peng], Liu, Y.H.[Yi-Hao], Wang, X.[Xiao], Li, B.[Bing], Hu, W.M.[Wei-Ming],
Learn to Match: Automatic Matching Network Design for Visual Tracking,
ICCV21(13319-13328)
IEEE DOI 2203
Training, Degradation, Visualization, Codes, Statistical analysis, Oceans, Motion and tracking, BibRef

Zheng, J.[Jilai], Ma, C.[Chao], Peng, H.[Houwen], Yang, X.K.[Xiao-Kang],
Learning to Track Objects from Unlabeled Videos,
ICCV21(13526-13535)
IEEE DOI 2203
Training, Codes, Dynamic programming, Object recognition, Unsupervised learning, Optical flow, Motion and tracking, BibRef

Xie, F.[Fei], Wang, C.Y.[Chun-Yu], Wang, G.[Guangting], Yang, W.K.[Wan-Kou], Zeng, W.J.[Wen-Jun],
Learning Tracking Representations via Dual-Branch Fully Transformer Networks,
VOT21(2688-2697)
IEEE DOI 2112
Target tracking, Costs, Fuses, Computational modeling, Graphics processing units, Transformers, Feature extraction BibRef

Liu, C.X.[Cheng-Xin], Cao, Z.G.[Zhi-Guo], Li, W.[Wei], Xiao, Y.[Yang], Du, S.[Shuaiyuan], Zhu, A.[Angfan],
Exploiting Distilled Learning for Deep Siamese Tracking,
ICPR21(577-583)
IEEE DOI 2105
Power demand, Pipelines, Memory management, Benchmark testing, Mobile handsets, Pattern recognition BibRef

Dai, P.[Peng], Weng, R.L.[Ren-Liang], Choi, W.[Wongun], Zhang, C.S.[Chang-Shui], He, Z.P.[Zhang-Ping], Ding, W.[Wei],
Learning a Proposal Classifier for Multiple Object Tracking,
CVPR21(2443-2452)
IEEE DOI 2111
Deep learning, Detectors, Market research, Trajectory, Computational efficiency, Proposals BibRef

Xu, Y., sep, A., Ban, Y., Horaud, R., Leal-Taixé, L., Alameda-Pineda, X.,
How to Train Your Deep Multi-Object Tracker,
CVPR20(6786-6795)
IEEE DOI 2008
Loss measurement, Training, Standards, Target tracking, Optimization, Neural networks BibRef

Li, Z., Xiong, F., Zhou, J., Wang, J., Lu, J., Qian, Y.,
BAE-Net: A Band Attention Aware Ensemble Network for Hyperspectral Object Tracking,
ICIP20(2106-2110)
IEEE DOI 2011
Videos, Hyperspectral imaging, Target tracking, Image color analysis, Object tracking, Machine learning, Color, ensemble learning BibRef

Zhang, D., Zheng, Z.,
High Performance Visual Tracking With Siamese Actor-Critic Network,
ICIP20(2116-2120)
IEEE DOI 2011
Training, Feature extraction, Visualization, Real-time systems, Learning (artificial intelligence), Target tracking, Robustness, Object Tracking BibRef

Wang, J., Wang, Y., Zhang, S., Xu, C., Deng, C.,
Dictionary Learning for Visual Tracking with Dimensionality Reduction,
ICIVC20(251-255)
IEEE DOI 2009
Target tracking, Visualization, Dictionaries, Robustness, Video sequences, Training, Appearance variation, Visual tracking, Target representation BibRef

Harley, A.W.[Adam W.], Lakshmikanth, S.K.[Shrinidhi Kowshika], Schydlo, P.[Paul], Fragkiadaki, K.[Katerina],
Tracking Emerges by Looking Around Static Scenes, with Neural 3d Mapping,
ECCV20(XXVI:598-614).
Springer DOI 2011
BibRef

Jin, J.T.[Jia-Ting], Li, X.W.[Xing-Wei], Li, X.L.[Xin-Long], Guan, S.J.[Shao-Jie],
Online Multi-object Tracking with Siamese Network and Optical Flow,
ICIVC20(193-198)
IEEE DOI 2009
Kalman filters, Feature extraction, Optical flow, Trajectory, Target tracking, Multi-object tracking, Siamese Network, DeepSORT BibRef

Weng, X., Wang, Y., Man, Y., Kitani, K.M.,
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning,
CVPR20(6498-6507)
IEEE DOI 2008
Feature extraction, Tracking, Neural networks, Pipelines, Training BibRef

Voigtlaender, P., Luiten, J., Torr, P.H.S., Leibe, B.,
Siam R-CNN: Visual Tracking by Re-Detection,
CVPR20(6577-6587)
IEEE DOI 2008
Heuristic algorithms, Feature extraction, Target tracking, Head, Benchmark testing, Dynamic programming BibRef

Brasó, G., Leal-Taixé, L.,
Learning a Neural Solver for Multiple Object Tracking,
CVPR20(6246-6256)
IEEE DOI 2008
Trajectory, Image edge detection, Task analysis, Message passing, Object tracking, Optimization, Object detection BibRef

Ruiz, I.[Idoia], Porzi, L.[Lorenzo], Bulò, S.R.[Samuel Rota], Kontschieder, P.[Peter], Serrat, J.[Joan],
Weakly Supervised Multi-Object Tracking and Segmentation,
WACVW21(125-133) Autonomous Vehicle Vision
IEEE DOI 2105
Training, Location awareness, Heating systems, Measurement, Image edge detection, Benchmark testing BibRef

Porzi, L.[Lorenzo], Hofinger, M., Ruiz, I.[Idoia], Serrat, J.[Joan], Bulò, S.R.[Samuel Rota], Kontschieder, P.[Peter],
Learning Multi-Object Tracking and Segmentation From Automatic Annotations,
CVPR20(6845-6854)
IEEE DOI 2008
Videos, Training data, Task analysis, Image segmentation, Pipelines, Optical imaging, Semantics BibRef

Ardö, H., Nilsson, M.,
Multi Target Tracking from Drones by Learning from Generalized Graph Differences,
VisDrone19(46-54)
IEEE DOI 2004
autonomous aerial vehicles, graph theory, learning (artificial intelligence), object detection, Multi target tracking BibRef

He, Z.[Zhen], Li, J.[Jian], Liu, D.[Daxue], He, H.[Hangen], Barber, D.[David],
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,
CVPR19(1318-1327).
IEEE DOI 2002
BibRef

Maksai, A.[Andrii], Fua, P.[Pascal],
Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking,
CVPR19(4634-4643).
IEEE DOI 2002
BibRef

Emambakhsh, M.[Mehryar], Bay, A.[Alessandro], Vazquez, E.[Eduard],
Deep Recurrent Neural Network for Multi-target Filtering,
MMMod19(II:519-531).
Springer DOI 1901
Results:
WWW Link. BibRef

Manh, H., Alaghband, G.,
Spatiotemporal KSVD Dictionary Learning for Online Multi-target Tracking,
CRV18(150-157)
IEEE DOI 1812
Dictionaries, Sparse matrices, Robots, Video sequences, Feature extraction, Color, multi-target tracking, online appearance learning BibRef

Ren, L.L.[Liang-Liang], Lu, J.W.[Ji-Wen], Wang, Z.F.[Zi-Feng], Tian, Q.[Qi], Zhou, J.[Jie],
Collaborative Deep Reinforcement Learning for Multi-object Tracking,
ECCV18(III: 605-621).
Springer DOI 1810
BibRef

Wan, X., Wang, J., Zhou, S.,
An Online and Flexible Multi-object Tracking Framework Using Long Short-Term Memory,
PBVS18(1311-13118)
IEEE DOI 1812
Trajectory, Tracking, Computational modeling, Kalman filters, Optical flow, Logic gates BibRef

Wan, X., Wang, J., Kong, Z., Zhao, Q., Deng, S.,
Multi-Object Tracking Using Online Metric Learning with Long Short-Term Memory,
ICIP18(788-792)
IEEE DOI 1809
Trajectory, Target tracking, Computational modeling, Kalman filters, Optical flow, Multiple Object Tracking, Data Association BibRef

Ullah, M., Alaya Cheikh, F.,
Deep Feature Based End-to-End Transportation Network for Multi-Target Tracking,
ICIP18(3738-3742)
IEEE DOI 1809
Target tracking, Trajectory, Optimization, Transportation, Dynamic programming, Feature extraction, Transportation network, multi-target tracking BibRef

Cui, Y.W.[Ya-Wen], Zhang, B.[Bo], Yang, W.J.[Wen-Jing], Wang, Z.Y.[Zhi-Yuan], Li, Y.[Yin], Yi, X.D.[Xiao-Dong], Tang, Y.H.[Yu-Hua],
End-to-End Visual Target Tracking in Multi-Robot Systems Based on Deep Convolutional Neural Network,
CEFR-LCV17(1113-1121)
IEEE DOI 1802
Angular velocity, Cameras, Feature extraction, Robot vision systems, Target tracking BibRef

Anh, N.T.L., Khan, F.M., Negin, F., Bremond, F.,
Multi-Object tracking using multi-channel part appearance representation,
AVSS17(1-6)
IEEE DOI 1806
Gaussian processes, feature extraction, image representation, learning (artificial intelligence), object detection, Trajectory BibRef

Gaidon, A.[Adrien], Wang, Q.[Qiao], Cabon, Y.[Yohann], Vig, E.[Eleonora],
VirtualWorlds as Proxy for Multi-object Tracking Analysis,
CVPR16(4340-4349)
IEEE DOI 1612
Learning, synthetic data. BibRef

Kieritz, H., Hübner, W., Arens, M.,
Joint Detection and Online Multi-object Tracking,
Joint18(1540-15408)
IEEE DOI 1812
Detectors, Recurrent neural networks, History, Object tracking, Feature extraction, Multilayer perceptrons BibRef

Sadeghian, A., Alahi, A., Savarese, S.,
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies,
ICCV17(300-311)
IEEE DOI 1802
learning (artificial intelligence), object detection, recurrent neural nets, sensor fusion, target tracking, Trajectory BibRef

Risse, B., Mangan, M., Webb, B., Pero, L.D.,
Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera,
Wildlife17(2840-2849)
IEEE DOI 1802
Animals, Cameras, Optimization, Target tracking, Visualization BibRef

Schulter, S., Vernaza, P., Choi, W., Chandraker, M.,
Deep Network Flow for Multi-object Tracking,
CVPR17(2730-2739)
IEEE DOI 1711
Bipartite graph, Cost function, Image edge detection, Neural networks, Trajectory BibRef

Dimou, A., Medentzidou, P., Álvarez García, F., Daras, P.,
Multi-target detection in CCTV footage for tracking applications using deep learning techniques,
ICIP16(928-932)
IEEE DOI 1610
Cameras BibRef

Chau, D.P.[Duc Phu], Subramanian, K., Brémond, F.[François],
Adaptive Neuro-Fuzzy Controller for Multi-object Tracker,
CVS15(466-476).
Springer DOI 1507
BibRef

Luo, W.H.[Wen-Han], Kim, T.K.[Tae-Kyun], Stenger, B.[Bjorn], Zhao, X.W.[Xiao-Wei], Cipolla, R.[Roberto],
Bi-label Propagation for Generic Multiple Object Tracking,
CVPR14(1290-1297)
IEEE DOI 1409
Multiple object tracking;clustered multi-task learning BibRef

Yan, W.[Wang], Han, X.Y.[Xiao-Ye], Pavlovic, V.[Vladimir],
Structured Learning for Multiple Object Tracking,
BMVC12(48).
DOI Link 1301
BibRef

Li, M.[Min], Chen, W.[Wei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Multi-Target Tracking by Learning Class-Specific and Instance-Specific Cues,
ACCV10(II: 67-81).
Springer DOI 1011
BibRef

Yuan, X.T.[Xiao-Tong], Li, S.Z.,
Learning Feature Extraction and Classification for Tracking Multiple Objects: A Unified Framework,
AVSBS06(22-22).
IEEE DOI 0611
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Online Tracking, Real Time Tracking Multiple Objects .