16.6.2.3.3 Target Tracking Techniques, Multiple Trackers, Multiple Models, Fusion

Chapter Contents (Back)
Target Tracking. Multi-Tracker. Motion Prediction. Fusion.

Bar-Shalom, Y., Chang, K.C., and Blom, H.A.,
Tracking a Maneuvering Target Using Input Estimation Versus the Interacting Multiple Model Algorithm,
AeroSys(25), No. 2, 1989, pp. 296-300. See also Tracking Maneuvering Targets with Multiple Sensors: Does More Data Always Mean Better Estimates?. BibRef 8900

Chang, K.C., Fung, R.,
Target Identification with Bayesian Networks in a Multiple Hypothesis Tracking System,
OptEng(36), No. 3, March 1997, pp. 684-691. 9704
BibRef

Cox, I.J.[Ingemar J.], Leonard, J.J.[John J.],
Modeling a Dynamic Environment Using a Multiple Hypothesis Approach,
AI(66), No. 2, March 1994, pp. 311-344.
Elsevier DOI BibRef 9403

Tissainayagam, P., Suter, D.,
Visual Tracking with Automatic Motion Model Switching,
PR(34), No. 3, March 2001, pp. 641-660.
Elsevier DOI 0101
BibRef
Earlier:
Visual Tracking of Multiple Objects with Automatic Motion Model Switching,
ICPR00(Vol III: 1134-1137).
IEEE DOI 0009
BibRef
Earlier:
Visual Tracking and Motion Determination Using the IMM Algorithm,
ICPR98(Vol I: 289-291).
IEEE DOI 9808
BibRef
Earlier:
Visual Feature Tracking with Automatic Motion Model Switching,
MVA98(xx-yy). BibRef

Tissainayagam, P., Suter, D.,
Performance Prediction Analysis of a Point Feature Tracker Based on Different Motion Models,
CVIU(84), No. 1, October 2001, pp. 104-125.
DOI Link 0203
BibRef

Tissainayagam, P.[Prithiraj], Suter, D.[David],
Object tracking in image sequences using point features,
PR(38), No. 1, January 2005, pp. 105-113.
Elsevier DOI 0410
BibRef

Tissainayagam, P.[Prithiraj], Suter, D.[David],
Performance Measures For Assessing Contour Trackers,
IJIG(2), No. 2, April 2002, pp. 343-359. 0204
BibRef
Earlier:
Empirical Evaluation on the Performance of Contour Trackers,
EEMCV01(xx-yy). 0110
BibRef

Tissainayagam, P.[Prithiraj], Suter, D.[David],
Contour tracking with automatic motion model switching,
PR(36No. 10, October 2003, pp. 2411-2427.
Elsevier DOI 0308
BibRef

Tissainayagam, P.[Prithiraj], Suter, D.[David],
Assessing the performance of corner detectors for point feature tracking applications,
IVC(22), No. 8, August 2004, pp. 663-679.
Elsevier DOI 0405
Corner Detector. BibRef

Shearer, K.[Kim], Wong, K.D.[Kirrily D.], Venkatesh, S.[Svetha],
Combining multiple tracking algorithms for improved general performance,
PR(34), No. 6, June 2001, pp. 1257-1269.
Elsevier DOI 0103
BibRef

McCane, B.[Brendan], Galvin, B.[Ben], Novins, K.[Kevin],
Algorithmic Fusion for More Robust Feature Tracking,
IJCV(49), No. 1, August 2002, pp. 79-89.
DOI Link 0209
BibRef

Toyama, K.[Kentaro], Horvitz, E.J.[Eric J.],
Modality fusion for object tracking with training system and method,
US_Patent6,502,082, Dec 31, 2002
WWW Link. BibRef 0212

Dawoud, A., Alam, M.S., Bal, A., Loo, C.,
Target Tracking in Infrared Imagery Using Weighted Composite Reference Function-Based Decision Fusion,
IP(15), No. 2, February 2006, pp. 404-410.
IEEE DOI 0602
BibRef

Wu, Y.[Ying], Huang, T.S.[Thomas S.],
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning,
IJCV(58), No. 1, June 2004, pp. 55-71.
DOI Link 0403
BibRef
Earlier:
A Co-inference Approach to Robust Visual Tracking,
ICCV01(II: 26-33).
IEEE DOI 0106
BibRef

Hua, G.[Gang], Wu, Y.[Ying],
Variational Maximum A Posteriori by Annealed Mean Field Analysis,
PAMI(27), No. 11, November 2005, pp. 1747-1761.
IEEE DOI 0510
BibRef
Earlier:
Multi-scale visual tracking by sequential belief propagation,
CVPR04(I: 826-833).
IEEE DOI 0408
Overcome abrupt changes in motion. BibRef

Hua, G.[Gang], Wu, Y.[Ying],
Sequential mean field variational analysis of structured deformable shapes,
CVIU(101), No. 2, February 2005, pp. 87-99.
Elsevier DOI 0512
BibRef

Hua, G.[Gang], Wu, Y.[Ying],
Measurement integration under inconsistency for robust tracking,
CVPR06(I: 650-657).
IEEE DOI 0606
BibRef

Veeraraghavan, H.[Harini], Schrater, P.[Paul], Papanikolopoulos, N.P.[Nikos P.],
Robust target detection and tracking through integration of motion, color, and geometry,
CVIU(103), No. 2, August 2006, pp. 121-138.
Elsevier DOI 0608
Multiple cue combination; Measurement error estimation; Expectation maximization; Data association BibRef

Brasnett, P.[Paul], Mihaylova, L.[Lyudmila], Bull, D.R.[David R.], Canagarajah, C.N.[C. Nishan],
Sequential Monte Carlo tracking by fusing multiple cues in video sequences,
IVC(25), No. 8, 1 August 2007, pp. 1217-1227.
Elsevier DOI 0706
Particle filtering; Tracking in video sequences; Colour; Texture; Edges; Multiple cues; Bhattacharyya distance See also Structural similarity-based object tracking in multimodality surveillance videos. BibRef

Vemula, M.[Mahesh], Bugallo, M.F.[Mónica F.], Djuric, P.M.[Petar M.],
Target tracking by fusion of random measures,
SIViP(1), No. 2, June 2007, pp. 149-161.
Springer DOI 0707
BibRef

Jia, Z.[Zhen], Balasuriya, A.[Arjuna], Challa, S.[Subhash],
Visual information fusion for object-based video image segmentation using unsupervised Bayesian online learning,
IET-IPR(1), No. 2, June 2007, pp. 168-181.
DOI Link 0905
BibRef

Olson, T.L.P.[Teresa Lorae Pace], Slaski, J.J.[James Joseph], Sanford, C.W.[Carl William], Han, R.Y.[Ruey-Yuan], Contini, C.L.[Casey Leonard], Reinig, R.R.[Robert Russell],
Real-time multi-stage infrared image-based tracking system,
US_Patent7,177,447, Feb 13, 2007
WWW Link. multiple trackers BibRef 0702

Tsechpenakis, C., Metaxas, D.N., Neidle, C.,
Combining Discrete and Continuous 3D Trackers,
HumMotBook08(6). 0802
BibRef

Jia, Z.[Zhen], Balasuriya, A.[Arjuna], Challa, S.[Subhash],
Vision based data fusion for autonomous vehicles target tracking using interacting multiple dynamic models,
CVIU(109), No. 1, January 2008, pp. 1-21.
Elsevier DOI 0801
BibRef
Earlier:
Camera motion and visual information fusion for 3D target tracking,
ICARCV04(III: 2297-2302).
IEEE DOI 0412
BibRef
Earlier: A1, A2, Only:
Motion based 3D Target Tracking with Interacting Multiple Linear Dynamic Models,
BMVC04(xx-yy).
HTML Version. 0508
Optical flow; Extended Kalman Filtering; Image segmentation and clustering; Stereo vision; Target tracking; Autonomous vehicles; Linear dynamics model; Kinematic model; Interacting multiple models (IMM); Pinhole camera projection model; Template matching and updating; Sensor data fusion BibRef

Leichter, I.[Ido], Lindenbaum, M.[Michael], Rivlin, E.[Ehud],
A General Framework for Combining Visual Trackers: The Black Boxes Approach,
IJCV(67), No. 3, May 2006, pp. 343-363.
Springer DOI 0606
BibRef
Earlier:
A probabilistic framework for combining tracking algorithms,
CVPR04(II: 445-451).
IEEE DOI 0408
BibRef
And:
A probabilistic cooperation between trackers of coupled objects,
ICIP04(II: 1045-1048).
IEEE DOI 0505
Formalized combination of multiple tracking results. BibRef

Leichter, I.[Ido], Lindenbaum, M.[Michael], Rivlin, E.[Ehud],
Bittracker: A Bitmap Tracker for Visual Tracking under Very General Conditions,
PAMI(30), No. 9, September 2008, pp. 1572-1588.
IEEE DOI 0808
Approximate a PDF for the object bitmap in each frame, and estimate the maximum change. No assumptions on motion of object or camera. BibRef

Leichter, I.[Ido], Lindenbaum, M.[Michael], Rivlin, E.[Ehud],
Tracking by Affine Kernel Transformations Using Color and Boundary Cues,
PAMI(31), No. 1, January 2009, pp. 164-171.
IEEE DOI 0812
BibRef
Earlier:
Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary,
ICCV07(1-6).
IEEE DOI 0710
BibRef

Leichter, I., Lindenbaum, M., Rivlin, E.,
The Cues in 'Dependent Multiple Cue Integration for Robust Tracking' Are Independent,
PAMI(36), No. 3, March 2014, pp. 620-621.
IEEE DOI 1403
See also Dependent Multiple Cue Integration for Robust Tracking. object tracking BibRef

Leichter, I.[Ido], Lindenbaum, M.[Michael], Rivlin, E.[Ehud],
Mean Shift tracking with multiple reference color histograms,
CVIU(114), No. 3, March 2010, pp. 400-408.
Elsevier DOI 1003
Visual tracking; Mean Shift; Multiple references BibRef

Leichter, I.[Ido],
Mean Shift Trackers with Cross-Bin Metrics,
PAMI(34), No. 4, April 2012, pp. 695-706.
IEEE DOI 1203
Cross-bin metrics rather than bin-by-bin metrics for histogram matching. BibRef

Kim, J.H.[Jung-Ho], Min, J.H.[Ji-Hong], Kweon, I.S.[In So], Lin, Z.[Zhe],
Fusing Multiple Independent Estimates via Spectral Clustering for Robust Visual Tracking,
SPLetters(19), No. 8, August 2012, pp. 527-530.
IEEE DOI 1208
BibRef

Zhao, P., Zhu, H.B., Li, H., Shibata, T.,
A Directional-Edge-Based Real-Time Object Tracking System Employing Multiple Candidate-Location Generation,
CirSysVideo(23), No. 3, March 2013, pp. 503-517.
IEEE DOI 1303
BibRef

Zhu, H.B.[Hong-Bo], Shibata, T.,
A Real-Time Motion-Feature-Extraction VLSI Employing Digital-Pixel-Sensor-Based Parallel Architecture,
CirSysVideo(24), No. 10, October 2014, pp. 1787-1799.
IEEE DOI 1411
CMOS image sensors BibRef

Zhong, B.N.[Bi-Neng], Yao, H.X.[Hong-Xun], Chen, S.[Sheng], Ji, R.R.[Rong-Rong], Chin, T.J.[Tat-Jun], Wang, H.Z.[Han-Zi],
Visual tracking via weakly supervised learning from multiple imperfect oracles,
PR(47), No. 3, 2014, pp. 1395-1410.
Elsevier DOI 1312
Visual tracking BibRef

Chen, Y.[Yan], Yang, X.N.[Xiang-Nan], Zhong, B.N.[Bi-Neng], Zhang, H.Z.[Hui-Zhen], Lin, C.L.[Chang-Long],
Network in network based weakly supervised learning for visual tracking,
JVCIR(37), No. 1, 2016, pp. 3-13.
Elsevier DOI 1603
Little supervision BibRef

Zhong, B.N.[Bi-Neng], Yao, H.X.[Hong-Xun], Chen, S.[Sheng], Ji, R.R.[Rong-Rong], Yuan, X.T.[Xiao-Tong], Liu, S.H.[Shao-Hui], Gao, W.[Wen],
Visual tracking via weakly supervised learning from multiple imperfect oracles 1,
CVPR10(1323-1330).
IEEE DOI 1006
BibRef

Nascimento, J.C.[Jacinto C.], Silva, J.G.[Jorge G.], Marques, J.S., Lemos, J.M.,
Manifold Learning for Object Tracking With Multiple Nonlinear Models,
IP(23), No. 4, April 2014, pp. 1593-1605.
IEEE DOI 1404
BibRef
Earlier: A1, A2, Only:
Manifold Learning for Object Tracking with Multiple Motion Dynamics,
ECCV10(III: 172-185).
Springer DOI 1009
Gaussian processes BibRef

Feldman-Haber, S., Keller, Y.,
A Probabilistic Graph-Based Framework for Plug-and-Play Multi-Cue Visual Tracking,
IP(23), No. 5, May 2014, pp. 2291-2301.
IEEE DOI 1405
Markov processes BibRef

Chiou, Y., Tsai, F.,
A Reduced-Complexity Data-Fusion Algorithm Using Belief Propagation for Location Tracking in Heterogeneous Observations,
Cyber(44), No. 6, June 2014, pp. 922-935.
IEEE DOI 1406
Accuracy BibRef

Davey, S.J.,
Efficient Histogram PMHT Via Single Target Chip Processing,
SPLetters(22), No. 5, May 2015, pp. 569-572.
IEEE DOI 1411
Approximation methods BibRef

Davey, S.J., Bessell, T., Cheung, B., Rutten, M.,
Track before Detect for Space Situation Awareness,
DICTA15(1-7)
IEEE DOI 1603
image registration BibRef

Vu, H.X., Davey, S.J.,
Track-Before-Detect Using Histogram PMHT and Dynamic Programming,
DICTA12(1-8).
IEEE DOI 1303
Probabilistic Multi-Hypothesis Tracker. BibRef

Bozorgtabar, B.[Behzad], Goecke, R.[Roland],
Efficient multi-target tracking via discovering dense subgraphs,
CVIU(144), No. 1, 2016, pp. 205-216.
Elsevier DOI 1604
BibRef
Earlier:
Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking,
ACCV14(V: 564-578).
Springer DOI 1504
BibRef
And:
Joint sparsity-based robust visual tracking,
ICIP14(4927-4931)
IEEE DOI 1502
BibRef
Earlier:
Robust Visual Tracking via Rank-Constrained Sparse Learning,
DICTA14(1-7)
IEEE DOI 1502
BibRef
And:
Discriminative Multi-Task Sparse Learning for Robust Visual Tracking Using Conditional Random Field,
DICTA14(1-8)
IEEE DOI 1502
BibRef
Earlier:
Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion,
DICTA13(1-8)
IEEE DOI 1402
Multi-target tracking. learning (artificial intelligence); computational complexity. Gaussian processes. Combine 2 trackers BibRef

Yoon, J.H.[Ju Hong], Yang, M.H.[Ming-Hsuan], Yoon, K.J.[Kuk-Jin],
Interacting Multiview Tracker,
PAMI(38), No. 5, May 2016, pp. 903-917.
IEEE DOI 1604
Algorithm design and analysis. BibRef

Ma, B., Hu, H., Shen, J., Liu, Y., Shao, L.,
Generalized Pooling for Robust Object Tracking,
IP(25), No. 9, September 2016, pp. 4199-4208.
IEEE DOI 1609
Gaussian processes BibRef

Ma, B.[Bo], Shen, J.B.[Jian-Bing], Liu, Y.B.[Yang-Biao], Hu, H.W.[Hong-Wei], Shao, L.[Ling], Li, X.L.[Xue-Long],
Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors,
MultMed(17), No. 10, October 2015, pp. 1818-1828.
IEEE DOI 1511
compressed sensing BibRef

Ma, B.[Bo], Huang, L., Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Discriminative Tracking Using Tensor Pooling,
Cyber(46), No. 11, November 2016, pp. 2411-2422.
IEEE DOI 1609
computer vision BibRef

Liu, Y.B.[Yang-Biao], Ma, B.[Bo], Hu, H.W.[Hong-Wei], Han, Y.[Yin],
Boosting-Based Visual Tracking Using Structural Local Sparse Descriptors,
ACCV14(V: 522-533).
Springer DOI 1504
BibRef

Gu, S., Ma, Z., Xie, M., Chen, Z.,
Online learning of mixture experts for real-time tracking,
IET-CV(10), No. 6, 2016, pp. 585-592.
DOI Link 1609
approximation theory BibRef

Vojir, T.[Tomas], Matas, J.[Jiri], Noskova, J.[Jana],
Online adaptive hidden Markov model for multi-tracker fusion,
CVIU(153), No. 1, 2016, pp. 109-119.
Elsevier DOI 1612
Visual tracking BibRef

Tian, X.L.[Xiao-Lin], Zhao, S.[Sujie], Jiao, L.C.[Li-Cheng], Gan, Z.P.[Zhi-Peng],
Nonnegative coding based ensemble tracking,
JVCIR(41), No. 1, 2016, pp. 166-175.
Elsevier DOI 1612
Object tracking BibRef

Li, Y., Jha, D.K., Ray, A., Wettergren, T.A.,
Information Fusion of Passive Sensors for Detection of Moving Targets in Dynamic Environments,
Cyber(47), No. 1, January 2017, pp. 93-104.
IEEE DOI 1612
Feature extraction BibRef

Li, J., Deng, C., Da Xu, R.Y., Tao, D., Zhao, B.,
Robust Object Tracking With Discrete Graph-Based Multiple Experts,
IP(26), No. 6, June 2017, pp. 2736-2750.
IEEE DOI 1705
graph theory, image filtering, neural nets, object tracking, optimisation, regression analysis, state estimation, support vector machines, TB-100, TB-50, VOT2015, base trackers, binary compatibility graph score, budget algorithm, current-tracker, deep convolutional neural network features, discrete graph-based multiple-experts, hand-crafted features, historical tracker snapshots, illumination changes, object state estimation, online SVM, regression correlation filters, robust object tracking, target appearance variation, target deformation, tracker drift, tracking drift, unary compatibility graph score, unified discrete graph optimization framework, Computational modeling, Correlation, Robustness, Support vector machines, Target tracking, Visualization, Object tracking, convolutional neural network, correlation filter, discrete graph, dynamic programming, support, vector, machine BibRef

Khalid, O., SanMiguel, J.C., Cavallaro, A.,
Multi-Tracker Partition Fusion,
CirSysVideo(27), No. 7, July 2017, pp. 1527-1539.
IEEE DOI 1707
Correlation, Fuses, Performance evaluation, Target tracking, Trajectory, Uncertainty, Decision Fusion, online performance evaluation, tracker correlation, visual, tracking BibRef

Hua, Y., Dong, X., Li, Q., Ren, Z.,
Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances,
Cyber(47), No. 8, August 2017, pp. 1959-1969.
IEEE DOI 1708
Multi-agent systems, Protocols, Robustness, Target tracking, Time-varying systems, Trajectory, Uncertain systems, Adaptive control, external disturbance, multiagent system, parameter uncertainty, time-varying, formation tracking BibRef

Rapuru, M.K., Kakanuru, S., Venugopal, P.M., Mishra, D., Subrahmanyam, G.R.K.S.[G. R. K. S.],
Correlation-Based Tracker-Level Fusion for Robust Visual Tracking,
IP(26), No. 10, October 2017, pp. 4832-4842.
IEEE DOI 1708
image fusion, learning (artificial intelligence), object tracking, ALOV300++, Kernelized correlation filter tracker, correlation-based tracker-level fusion, frame-level detection strategy, robust visual tracking, visual object tracking algorithms, visual tracker benchmark, Correlation, Detectors, Robustness, Target tracking, Training, Visualization, Correlation measure, detection, learning, trackers fusion. BibRef

Gundogdu, E.[Erhan], Ozkan, H.[Huseyin], Alatan, A.A.[A. Aydin],
Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing,
IP(26), No. 11, November 2017, pp. 5270-5283.
IEEE DOI 1709
BibRef
Earlier:
Ensemble Of adaptive correlation filters for robust visual tracking,
AVSS16(15-22)
IEEE DOI 1611
trees (mathematics), binary tree, tree-structured ensemble, Target tracking, ensemble tracker, mixture of experts BibRef

Gundogdu, E., Koc, A., Solmaz, B., Hammoud, R.I., Alatan, A.A.,
Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum,
PBVS16(290-298)
IEEE DOI 1612
BibRef

Gundogdu, E.[Erhan], Ozkan, H.[Huseyin], Demir, H.S.[H. Seckin], Ergezer, H.[Hamza], Akagunduz, E.[Erdem], Pakin, S.K.[S. Kubilay],
Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance,
PBVS15(1-9)
IEEE DOI 1510
Correlators BibRef

Tanisik, G.[Gokhan], Gundogdu, E.[Erhan],
Multiple model adaptive visual tracking with correlation filters,
ICIP15(661-665)
IEEE DOI 1512
adaptive update rate; multiple model; visual tracking BibRef


Shin, H.[Hyunhak], Cho, C.[Chuljin], Ko, H.[Hanseok],
Single object tracking based on active and passive detection information in distributed heterogeneous sensor network,
AVSS16(444-449)
IEEE DOI 1611
Mathematical model BibRef

Ma, L., Lu, J., Feng, J., Zhou, J.,
Multiple Feature Fusion via Weighted Entropy for Visual Tracking,
ICCV15(3128-3136)
IEEE DOI 1602
Computational modeling BibRef

Moujtahid, S.[Salma], Duffner, S.[Stefan], Baskurt, A.[Atilla],
Classifying Global Scene Context for On-line Multiple Tracker Selection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Demir, H.S.[H. Seckin], Cetin, A.E.,
Co-difference based object tracking algorithm for infrared videos,
ICIP16(434-438)
IEEE DOI 1610
Covariance matrices BibRef

Lee, D.Y.[Dae-Youn], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Multihypothesis trajectory analysis for robust visual tracking,
CVPR15(5088-5096)
IEEE DOI 1510
BibRef

Wang, X., Valstar, M.F.[Michel F.], Martinez, B., Khan, M.H.[Muhammad H.], Pridmore, T.P.[Tony P.],
TRIC-track: Tracking by Regression with Incrementally Learned Cascades,
ICCV15(4337-4345)
IEEE DOI 1602
Adaptation models BibRef

Khan, M.H.[Muhammad H.], Valstar, M.F.[Michel F.], Pridmore, T.P.[Tony P.],
A Generalized Search Method for Multiple Competing Hypotheses in Visual Tracking,
ICPR14(2245-2250)
IEEE DOI 1412
Accuracy BibRef

Nguyen, T.[Tuan], Pridmore, T.P.[Tony P.],
Tracking Using Multiple Linear Searches and Motion Direction Sampling,
ICPR14(2191-2196)
IEEE DOI 1412
Adaptation models BibRef

Bailer, C.[Christian], Stricker, D.[Didier], Bailer, C., Stricker, D.,
Tracker Fusion on VOT Challenge: How Does It Perform and What Can We Learn about Single Trackers?,
VOT15(630-638)
IEEE DOI 1602
Buildings BibRef

Bailer, C.[Christian], Pagani, A.[Alain], Stricker, D.[Didier],
A Superior Tracking Approach: Building a Strong Tracker through Fusion,
ECCV14(VII: 170-185).
Springer DOI 1408
BibRef

Reyna-Ayala, E.[Edgar], Conant-Pablos, S.E.[Santiago E.], Terashima-Marín, H.[Hugo],
Assembling Similar Tracking Approaches in Order to Strengthen Performance,
MCPR14(201-210).
Springer DOI 1407
BibRef

Chau, D.P.[Duc Phu], Bremond, F.[Francois], Thonnat, M.[Monique],
Automatic tracker selection w.r.t object detection performance,
WACV14(870-876)
IEEE DOI 1406
Color BibRef

Chen, W.H.[Wei-Hua], Cao, L.J.[Li-Jun], Zhang, J.G.[Jun-Ge], Huang, K.Q.[Kai-Qi],
An Adaptive Combination of Multiple Features for Robust Tracking in Real Scene,
VOT13(129-136)
IEEE DOI 1403
feature extraction BibRef

Qin, L.[Lei], Snoussi, H.[Hichem], Abdallah, F.[Fahed],
Cascaded Generative and Discriminative Learning for Visual Tracking,
ICIAR13(397-406).
Springer DOI 1307
BibRef

Li, Q.N.[Quan-Nan], Wang, X.G.[Xing-Gang], Wang, W.[Wei], Jiang, Y.[Yuan], Zhou, Z.H.[Zhi-Hua], Tu, Z.W.[Zhuo-Wen],
Disagreement-Based Multi-system Tracking,
DTCE12(II:320-334).
Springer DOI 1304
BibRef

Lyu, C.X.[Chao-Xin], Yan, Y.[Yan], Wang, H.Z.[Han-Zi],
Robust visual tracking with the cross-bin metric,
ICPR12(2120-2123).
WWW Link. 1302
BibRef

Patzold, M.[Michael], Evangelio, R.H.[Ruben Heras], Sikora, T.[Thomas],
Boosting Multi-hypothesis Tracking by Means of Instance-Specific Models,
AVSS12(416-421).
IEEE DOI 1211
BibRef

Yan, X.[Xu], Wu, X.[Xuqing], Kakadiaris, I.A.[Ioannis A.], Shah, S.K.[Shishir K.],
To Track or To Detect? An Ensemble Framework for Optimal Selection,
ECCV12(V: 594-607).
Springer DOI 1210
BibRef

Park, M.G.[Min-Gyu], Yoon, K.J.[Kuk-Jin],
Efficient Point Feature Tracking based on Self-aware Distance Transform,
BMVC12(32).
DOI Link 1301
BibRef

Yoon, J.H.[Ju Hong], Kim, D.Y.[Du Yong], Yoon, K.J.[Kuk-Jin],
Visual Tracking via Adaptive Tracker Selection with Multiple Features,
ECCV12(IV: 28-41).
Springer DOI 1210
BibRef

García, G.M.[Germán Martín], Klein, D.A.[Dominik Alexander], Stückler, J.[Jörg], Frintrop, S.[Simone], Cremers, A.B.[Armin B.],
Adaptive Multi-Cue 3D Tracking of Arbitrary Objects,
DAGM12(357-366).
Springer DOI 1209
BibRef

Gu, X.[Xin], Wang, H.T.[Hai-Tao], Wang, L.F.[Ling-Feng], Pan, C.H.[Chun-Hong],
Adaptive multi-cue fusion for visual target tracking based on uncertainly measure,
IVCNZ10(1-8).
IEEE DOI 1203
BibRef

Wilk, S.[Stefan], Kopf, S.[Stephan], Effelsberg, W.[Wolfgang],
Robust tracking for interactive social video,
WACV12(105-110).
IEEE DOI 1203
In interactive multimedia systems. Combined 3 distinct tracking techniques. See also Histogram-based image registration for real-time high dynamic range videos. BibRef

Lakshman, H.[Haricharan], Schwarz, H.[Heiko], Wiegand, T.[Thomas],
Adaptive motion model selection using a cubic spline based estimation framework,
ICIP10(805-808).
IEEE DOI 1009
BibRef

Li, M.[Mu], Kwok, J.T.[James T.], Lu, B.L.[Bao-Liang],
Online multiple instance learning with no regret,
CVPR10(1395-1401).
IEEE DOI 1006
Adapt a batch approach to an iterative approach for tracking. BibRef

Lin, F.[Feng], Chen, B.M.[Ben M.], Lee, T.H.[Tong H.],
Robust Vision-Based Target Tracking Control System for an Unmanned Helicopter Using Feature Fusion,
MVA09(398-).
PDF File. 0905
BibRef

Streib, K.[Kevin], Davis, J.W.[James W.],
Exploiting Multiple Cameras for Environmental Pathlets,
ISVC10(III: 613-624).
Springer DOI 1011
BibRef
And:
Extracting Pathlets from Weak Tracking Data,
AVSS10(353-360).
IEEE DOI 1009
See also Summarizing high-level scene behavior. BibRef

Strandmark, P.[Petter], Gu, I.Y.H.[Irene Y. H.],
Joint Random Sample Consensus and Multiple Motion Models for Robust Video Tracking,
SCIA09(450-459).
Springer DOI 0906
BibRef

Yin, Z.Z.[Zhao-Zheng], Porikli, F.M.[Fatih M.], Collins, R.T.[Robert T.],
Likelihood Map Fusion for Visual Object Tracking,
WACV08(1-7).
IEEE DOI 0801
BibRef

Chen, J.X.[Ji-Xu], Ji, Q.A.[Qi-Ang],
Online Spatial-temporal Data Fusion for Robust Adaptive Tracking,
Learning07(1-8).
IEEE DOI 0706
BibRef

Lacey, A.J., Thacker, N.A., Seed, N.L.,
Feature Tracking and Motion Classification Using a Switchable Model Kalman Filter,
BMVC94(xx-yy).
PDF File. 9409
BibRef

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


Last update:Sep 22, 2017 at 21:00:01