16.6.2.20 Target Tracking Techniques, Performance Evaluation, Comparison, Benchmarks, Datasets, Survey

Hopkins 155,
Motion Dataset Online2007.
WWW Link. Dataset, Motion. Testing feature based motion segmentation algorithms.
See also Johns Hopkins University. BibRef 0700

Tracking Any Object, TAO, Dataset,
Motion Dataset Online
WWW Link. Dataset, Tracking. 2,907 high resolution videos, captured in diverse environments. BibRef

Electro-Optical Imaging, Inc.,

WWW Link. Vendor, Tracking.

Imago,
1987.
WWW Link. Vendor, Tracking.

PerceptiVu, Inc.,
2002.
HTML Version. Vendor, Tracking.

Polehemus, Motion Tracking,
2005.
WWW Link. Vendor, Motion Tracking. Also does eye tracking and 3-D Laser Scanner

OTCBVS Benchmark Dataset Collection,
2001
WWW Link. Dataset, Tracking. Dataset, Face Recognition. Collection of datasets for benchmarking realted to the related conferences. Includes face dataset.

UCF Parking Lot Tracking,
2012
WWW Link. Dataset, Tracking. Tracking multiple people in parking lot.
See also Part-based multiple-person tracking with partial occlusion handling.
See also GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs.

Nagarajan, V., Chideambara, M.R., and Sharma, R.N.,
Combinatorial Problems in Multitarget Tracking: A Comprehensive Survey,
IEE-P(F: 134), No. 1, 1987, pp. 113-118. Survey, Target Tracking. BibRef 8700

Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J.,
Interacting Multiple Model Methods in Target Tracking: A Survey,
AeroSys(34), No. 1, January 1998, pp. 103-123. 9801
Survey, Target Tracking. BibRef

Sundareshan, M.K., Amoozegar, F.,
Neural-Network Fusion Capabilities for Efficient Implementation of Tracking Algorithms,
OptEng(36), No. 3, March 1997, pp. 692-707. 9704
BibRef

Amoozegar, F.,
Neural-Network-Based Target Tracking State-of-the-Art Survey,
OptEng(37), No. 3, March 1998, pp. 836-846. 9804
Survey, Target Tracking. BibRef

Lepetit, V.[Vincent], Fua, P.[Pascal],
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey,
FTCGV(1), Issue 1, 2005, pp. 1-89.
DOI Link 1410
Survey, Tracking. Published August 2005. BibRef

Yilmaz, A.[Alper], Javed, O.[Omar], Shah, M.[Mubarak],
Object tracking: A survey,
Surveys(38), No. 4 2006, pp. 13.
WWW Link. 0701
Survey, Target Tracking. BibRef

Bernardin, K.[Keni], Stiefelhagen, R.[Rainer],
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0811
BibRef

Stiefelhagen, R.[Rainer], Bernardin, K.[Keni], Bowers, R.[Rachel], Rose, R.T.[R. Travis], Michel, M.[Martial], Garofolo, J.[John],
The CLEAR 2007 Evaluation,
MTPH07(xx-yy).
Springer DOI 0705
BibRef

Abidi, B.R.[Besma R.], Aragam, N.R.[Nash R.], Yao, Y.[Yi], Abidi, M.A.[Mongi A.],
Survey and analysis of multimodal sensor planning and integration for wide area surveillance,
Surveys(41), No. 1, December 2008, pp. 1-36.
WWW Link. 0804
Survey, Sensor Planning. BibRef

Sankaranarayanan, A.C.[Aswin C.], Veeraraghavan, A., Chellappa, R.[Rama],
Object Detection, Tracking and Recognition for Multiple Smart Cameras,
PIEEE(96), No. 10, October 2008, pp. 1606-1624.
IEEE DOI 0811
BibRef

Sankaranarayanan, A.C.[Aswin C.], Chellappa, R.[Rama],
Optimal Multi-View Fusion of Object Locations,
Motion08(1-8).
IEEE DOI 0801
BibRef

Wu, H.[Hao], Chellappa, R.[Rama], Sankaranarayanan, A.C.[Aswin C.], Zhou, S.H.K.[Shao-Hua Kevin],
Robust Visual Tracking Using the Time-Reversibility Constraint,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Wu, H.[Hao], Sankaranarayanan, A.C.[Aswin C.], Chellappa, R.[Rama],
Online Empirical Evaluation of Tracking Algorithms,
PAMI(32), No. 8, August 2010, pp. 1443-1458.
IEEE DOI 1007
BibRef
Earlier:
In Situ Evaluation of Tracking Algorithms Using Time Reversed Chains,
CVPR07(1-8).
IEEE DOI 0706
Without initial ground truth. BibRef

Kasturi, R.[Rangachar], Goldgof, D.B.[Dmitry B.], Soundararajan, P.[Padmanabhan], Manohar, V.[Vasant], Garofolo, J.[John], Bowers, R.[Rachel], Boonstra, M.[Matthew], Korzhova, V.N.[Valentina N.], Zhang, J.[Jing],
Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol,
PAMI(31), No. 2, February 2009, pp. 319-336.
IEEE DOI 0901
BibRef

Soundararajan, P.[Padmanabhan], Boonstra, M.[Matthew], Manohar, V.[Vasant], Korzhova, V.N.[Valentina N.], Goldgof, D.B.[Dmitry B.], Kasturi, R.[Rangachar], Prasad, S.[Shubha], Raju, H.[Harish], Bowers, R.[Rachel], Garofolo, J.[John],
Evaluation Framework for Video OCR,
ICCVGIP06(829-836).
Springer DOI 0612
BibRef

Manohar, V.[Vasant], Soundararajan, P.[Padmanabhan], Raju, H.[Harish], Goldgof, D.[Dmitry], Kasturi, R.[Rangachar], Garofolo, J.[John],
Performance Evaluation of Object Detection and Tracking in Video,
ACCV06(II:151-161).
Springer DOI 0601
BibRef

Manohar, V.[Vasant], Soundararajan, P.[Padmanabhan], Boonstra, M.[Matthew], Raju, H.[Harish], Goldgof, D.[Dmitry], Kasturi, R.[Rangachar], Garofolo, J.[John],
Performance Evaluation of Text Detection and Tracking in Video,
DAS06(576-587).
Springer DOI 0602
BibRef

San Miguel, J.C., Cavallaro, A., Martinez, J.M.,
Adaptive Online Performance Evaluation of Video Trackers,
IP(21), No. 5, May 2012, pp. 2812-2823.
IEEE DOI 1204
BibRef

Yin, F.[Fei], Makris, D.[Dimitrios], Velastin, S.A.[Sergio A.], Orwell, J.[James],
Quantitative evaluation of different aspects of motion trackers under various challenges,
BMVA(2010), No. 5, 2010, pp. 1-11.
PDF File. 1209
BibRef

Carvalho, P.[Pedro], Cardoso, J.S.[Jaime S.], Corte-Real, L.[Luís],
Filling the gap in quality assessment of video object tracking,
IVC(30), No. 9, September 2012, pp. 630-640.
Elsevier DOI 1210
Tracking; Algorithm assessment; Evaluation metrics; Information fusion BibRef

San Miguel, J.C.[Juan C.], Cavallaro, A.[Andrea], Martinez, J.M.[Jose M.],
Standalone evaluation of deterministic video tracking,
ICIP12(1353-1356).
IEEE DOI 1302
BibRef
Earlier:
Evaluation of on-line quality estimators for object tracking,
ICIP10(825-828).
IEEE DOI 1009

See also semantic-guided and self-configurable framework for video analysis, A. BibRef

Nawaz, T.[Tahir], Cavallaro, A.[Andrea],
A Protocol for Evaluating Video Trackers Under Real-World Conditions,
IP(22), No. 4, April 2013, pp. 1354-1361.
IEEE DOI 1303
BibRef
Earlier:
PFT: A protocol for evaluating video trackers,
ICIP11(2325-2328).
IEEE DOI 1201
BibRef

Liu, Q.[Qi], Zhao, X.G.[Xiao-Guang], Hou, Z.G.[Zeng-Guang],
Survey of single-target visual tracking methods based on online learning,
IET-CV(8), No. 5, October 2014, pp. 419-428.
DOI Link 1412
learning (artificial intelligence) BibRef

Franz, A.M.[Alfred Michael], Haidegger, T., Birkfellner, W., Cleary, K., Peters, T.M., Maier-Hein, L.[Lena],
Electromagnetic Tracking in Medicine: A Review of Technology, Validation, and Applications,
MedImg(33), No. 8, August 2014, pp. 1702-1725.
IEEE DOI 1408
Biomedical imaging BibRef

Wu, Y.[Yi], Lim, J.W.[Jong-Woo], Yang, M.H.[Ming-Hsuan],
Object Tracking Benchmark,
PAMI(37), No. 9, September 2015, pp. 1834-1848.
IEEE DOI 1508
BibRef
Earlier:
Online Object Tracking: A Benchmark,
CVPR13(2411-2418)
IEEE DOI 1309
Algorithm design and analysis. BibRef

Dubuisson, S.[Séverine], Gonzales, C.[Christophe],
A survey of datasets for visual tracking,
MVA(27), No. 1, January 2016, pp. 23-52.
WWW Link. 1601
Survey, Tracking. Dataset, Tracking. BibRef

Kristan, M.[Matej], Matas, J.G.[Jiri G.], Leonardis, A.[Ales], Vojír, T.[Tomáš], Pflugfelder, R.[Roman], Fernández, G.[Gustavo], Nebehay, G.[Georg], Porikli, F.M.[Fatih M.], Cehovin, L.[Luka],
A Novel Performance Evaluation Methodology for Single-Target Trackers,
PAMI(38), No. 11, November 2016, pp. 2137-2155.
IEEE DOI 1610
Benchmark testing BibRef

Cehovin, L.[Luka], Kristan, M.[Matej], Leonardis, A.[Ales],
Is my new tracker really better than yours?,
WACV14(540-547)
IEEE DOI 1406
Accuracy BibRef

Souza, É.L.[Éfren L.], Nakamura, E.F.[Eduardo F.], Pazzi, R.W.[Richard W.],
Target Tracking for Sensor Networks: A Survey,
Surveys(48), No. 3, February 2016, pp. 30.
DOI Link 1612
Survey, Tracking. Use three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Organize tracking into six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. BibRef

Zhang, B.C.[Bao-Chang], Li, Z.G.[Zhi-Gang], Perina, A.[Alessandro], del Bue, A.[Alessio], Murino, V.[Vittorio], Liu, J.,
Adaptive Local Movement Modeling for Robust Object Tracking,
CirSysVideo(27), No. 7, July 2017, pp. 1515-1526.
IEEE DOI 1707
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
Adaptive Local Movement Modelling for Object Tracking,
WACV15(25-32)
IEEE DOI 1503
Adaptation models, Boosting, Gravity, Object tracking, Robustness, Target tracking, Gaussian mixture model (GMM), online learning, tracking. Adaptation models BibRef

Tapiero, J.E.[Juan E.], Medeiros, H.[Henry], Bishop, R.H.[Robert H.],
Predicting multiple target tracking performance for applications on video sequences,
MVA(28), No. 5-6, August 2017, pp. 539-550.
WWW Link. 1708
BibRef

Nawaz, T.[Tahir], Ellis, A.[Anna], Ferryman, J.M.[James M.],
A method for performance diagnosis and evaluation of video trackers,
SIViP(11), No. 7, October 2017, pp. 1287-1295.
WWW Link. 1708
BibRef

Li, P.X.[Pei-Xia], Wang, D.[Dong], Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan],
Deep visual tracking: Review and experimental comparison,
PR(76), No. 1, 2018, pp. 323-338.
Elsevier DOI 1801
Visual tracking BibRef

Liu, F., Gong, C., Huang, X., Zhou, T., Yang, J., Tao, D.,
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching,
IP(27), No. 6, June 2018, pp. 2777-2790.
IEEE DOI 1804
correlation methods, image filtering, image matching, image representation, object detection, template matching BibRef

Queirós, S., Morais, P., Barbosa, D., Fonseca, J.C., Vilaça, J.L., d'Hooge, J.,
MITT: Medical Image Tracking Toolbox,
MedImg(37), No. 11, November 2018, pp. 2547-2557.
IEEE DOI 1811
Code, Tracking. Medical diagnostic imaging, Target tracking, Image sequences, Matlab, motion estimation BibRef

Guan, H.[Hao], Cheng, B.Z.[Bao-Zhong],
How do deep convolutional features affect tracking performance: An experimental study,
VC(34), No. 12, December 2018, pp. 1701-1711.
WWW Link. 1811
BibRef

El-Shafie, A.H.A.[Al-Hussein A.], Habib, S.E.D.[Serag E.D.],
Survey on hardware implementations of visual object trackers,
IET-IPR(13), No. 6, 10 May 2019, pp. 863-876.
DOI Link 1906
BibRef

Fiaz, M.[Mustansar], Mahmood, A.[Arif], Javed, S.[Sajid], Jung, S.K.[Soon Ki],
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends,
Surveys(51), No. 1, February 2019, pp. Article No 43.
DOI Link 1906
Survey, Tracking. BibRef

Xu, Y.K.[Ying-Kun], Zhou, X.L.[Xiao-Long], Chen, S.Y.[Sheng-Yong], Li, F.F.[Fen-Fen],
Deep learning for multiple object tracking: a survey,
IET-CV(13), No. 4, June 2019, pp. 355-368.
DOI Link 1906
BibRef

Guo, Q., Feng, W., Gao, R., Liu, Y., Wang, S.,
Exploring the Effects of Blur and Deblurring to Visual Object Tracking,
IP(30), 2021, pp. 1812-1824.
IEEE DOI 2101
Tracking, Benchmark testing, Object tracking, Visualization, Target tracking, Robustness, Video tracking, deblurring BibRef

Fan, H.[Heng], Bai, H.X.[He-Xin], Lin, L.T.[Li-Ting], Yang, F.[Fan], Chu, P.[Peng], Deng, G.[Ge], Yu, S.J.[Si-Jia], Harshit, Huang, M.Z.[Ming-Zhen], Liu, J.H.[Jue-Huan], Xu, Y.[Yong], Liao, C.Y.[Chun-Yuan], Yuan, L.[Lin], Ling, H.B.[Hai-Bin],
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark,
IJCV(129), No. 2, February 2021, pp. 439-461.
Springer DOI 2102
BibRef
Earlier: A1, A3, A4, A5, A6, A7, A2, A11, A12, A14, Only:
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking,
CVPR19(5369-5378).
IEEE DOI 2002
BibRef

Liu, X.R.[Xin-Ran], Liu, X.Q.[Xiao-Qiong], Yi, Z.[Ziruo], Zhou, X.[Xin], Le, T.[Thanh], Zhang, L.[Libo], Huang, Y.[Yan], Yang, Q.[Qing], Fan, H.[Heng],
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking,
ICCV23(20392-20401)
IEEE DOI Code:
WWW Link. 2401
BibRef

Huang, L.H.[Liang-Hua], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild,
PAMI(43), No. 5, May 2021, pp. 1562-1577.
IEEE DOI 2104

WWW Link. Dataset, Tracking. Training, Object tracking, Databases, Protocols, Benchmark testing, Servers, Object tracking, benchmark dataset, performance evaluation BibRef

Luo, W.H.[Wen-Han], Xing, J.L.[Jun-Liang], Milan, A.[Anton], Zhang, X.Q.[Xiao-Qin], Liu, W.[Wei], Kim, T.K.[Tae-Kyun],
Multiple object tracking: A literature review,
AI(293), 2021, pp. 103448.
Elsevier DOI 2103
Survey, Tracking. Multi-object tracking, Data association, Survey BibRef

Marvasti-Zadeh, S.M.[Seyed Mojtaba], Cheng, L.[Li], Ghanei-Yakhdan, H.[Hossein], Kasaei, S.[Shohreh],
Deep Learning for Visual Tracking: A Comprehensive Survey,
ITS(23), No. 5, May 2022, pp. 3943-3968.
IEEE DOI 2205
Visualization, Target tracking, Tracking, Training, Benchmark testing, Feature extraction, Correlation, appearance modeling BibRef

Zhang, Z.X.[Zhao-Xiang], Wang, C.H.[Cheng-Hang], Song, J.N.[Jia-Ning], Xu, Y.L.[Yue-Lei],
Object Tracking Based on Satellite Videos: A Literature Review,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Chen, F.[Fei], Wang, X.D.[Xiao-Dong], Zhao, Y.X.[Yun-Xiang], Lv, S.H.[Shao-He], Niu, X.[Xin],
Visual object tracking: A survey,
CVIU(222), 2022, pp. 103508.
Elsevier DOI 2209
Object tracking, Discriminative trackers, Deep neural networks BibRef

Chen, Y.Z.[Yu-Zeng], Tang, Y.Q.[Yu-Qi], Xiao, Y.[Yi], Yuan, Q.Q.[Qiang-Qiang], Zhang, Y.W.[Yu-Wei], Liu, F.Q.[Feng-Qing], He, J.[Jiang], Zhang, L.P.[Liang-Pei],
Satellite video single object tracking: A systematic review and an oriented object tracking benchmark,
PandRS(210), 2024, pp. 212-240.
Elsevier DOI Code:
WWW Link. 2404
Satellite video, Deep learning, Correlation filter, Single object tracking, Benchmark BibRef

Zhao, X.[Xin], Hu, S.Y.[Shi-Yu], Wang, Y.[Yipei], Zhang, J.[Jing], Hu, Y.M.[Yi-Min], Liu, R.[Rongshuai], Ling, H.B.[Hai-Bin], Li, Y.[Yin], Li, R.[Renshu], Liu, K.[Kun], Li, J.[Jiadong],
BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision,
IJCV(132), No. 5, May 2024, pp. 1659-1684.
Springer DOI 2405
BibRef

Zhao, S.C.[Shao-Chuan], Xu, T.Y.[Tian-Yang], Wu, X.J.[Xiao-Jun], Kittler, J.V.[Josef V.],
A Spatio-Temporal Robust Tracker with Spatial-Channel Transformer and Jitter Suppression,
IJCV(132), No. 5, May 2024, pp. 1645-1658.
Springer DOI 2405
Both position accuracy and smoothness of trajectory. BibRef

Sun, D.D.[Deng-Di], Cheng, L.L.[Lei-Lei], Chen, S.[Song], Li, C.L.[Cheng-Long], Xiao, Y.[Yun], Luo, B.[Bin],
UAV-Ground Visual Tracking: A Unified Dataset and Collaborative Learning Approach,
CirSysVideo(34), No. 5, May 2024, pp. 3619-3632.
IEEE DOI Code:
WWW Link. 2405
Target tracking, Visualization, Autonomous aerial vehicles, Object tracking, Task analysis, Fuses, Video sequences, collaborative learning BibRef

Brewczynski, K.D.[Konrad D.], Zyczkowski, M.[Marek], Cichulski, K.[Krzysztof], Kaminski, K.A.[Kamil A.], Petsioti, P.[Paraskevi], de Cubber, G.[Geert],
Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems,
RS(16), No. 19, 2024, pp. 3714.
DOI Link 2410
evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) UAVs BibRef

Li, N.[Ning], Zhong, B.[Bineng], Zheng, Y.Z.[Yao-Zong], Liang, Q.H.[Qi-Hua], Mo, Z.Y.[Zhi-Yi], Song, S.X.[Shu-Xiang],
Robust Tracking via Combing Top-Down and Bottom-Up Attention,
CirSysVideo(34), No. 10, October 2024, pp. 9774-9785.
IEEE DOI 2411
Visualization, Transformers, Task analysis, Bayes methods, Target tracking, Feature extraction, Generators, Object tracking, top-down BibRef

Xie, J.X.[Jin-Xia], Zhong, B.N.[Bi-Neng], Mo, Z.Y.[Zhi-Yi], Zhang, S.P.[Sheng-Ping], Shi, L.T.[Liang-Tao], Song, S.X.[Shu-Xiang], Ji, R.R.[Rong-Rong],
Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers,
CVPR24(19300-19309)
IEEE DOI Code:
WWW Link. 2410
Adaptation models, Visualization, Target tracking, Computational modeling, Transformers BibRef

Zhang, L.[Lian], Wang, L.X.[Ling-Xue], Wu, Y.Z.[Yu-Zhen], Chen, M.K.[Ming-Kun], Zheng, D.Z.[De-Zhi], Cao, L.C.[Liang-Cai], Zeng, B.Z.[Bang-Ze], Cai, Y.[Yi],
UniRTL: A universal RGBT and low-light benchmark for object tracking,
PR(158), 2025, pp. 110984.
Elsevier DOI 2411
RGBT and low-light benchmark, Multitask benchmark, Unified object tracking, RGBT and low-light image BibRef

Wang, P.F.[Peng-Fei], Hui, X.F.[Xiao-Fei], Wu, J.[Jing], Yang, Z.[Zile], Ong, K.E.[Kian Eng], Zhao, X.G.[Xin-Ge], Lu, B.[Beijia], Huang, D.[Dezhao], Ling, E.[Evan], Chen, W.L.[Wei-Ling], Ma, K.T.[Keng Teck], Hur, M.[Minhoe], Liu, J.[Jun],
Semtrack: A Large-scale Dataset for Semantic Tracking in the Wild,
ECCV24(XXIV: 486-504).
Springer DOI 2412
BibRef

Woo, S.[Sanghyun], Park, K.Y.[Kwan-Yong], Shin, I.[Inkyu], Kim, M.[Myungchul], Kweon, I.S.[In So],
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark,
CVPR24(22335-22346)
IEEE DOI 2410
Visualization, Technological innovation, Surveillance, Video sequences, Benchmark testing, Streaming media, Cameras, MTMC, Tracking BibRef

Gloudemans, D.[Derek], Zachár, G.[Gergely], Wang, Y.B.[Yan-Bing], Ji, J.[Junyi], Nice, M.[Matt], Bunting, M.[Matt], Barbour, W.W.[William W.], Sprinkle, J.[Jonathan], Piccoli, B.[Benedetto], Monache, M.L.D.[Maria Laura Delle], Bayen, A.[Alexandre], Seibold, B.[Benjamin], Work, D.B.[Daniel B.],
So you think you can track?,
WACV24(4516-4526)
IEEE DOI 2404
Road transportation, Measurement, Transportation, Object detection, Benchmark testing, Cameras, Trajectory, Algorithms, Video recognition and understanding BibRef

Feng, W.Y.[Wei-Yu], Zhao, S.Z.[Seth Z.], Pan, C.[Chuanyu], Chang, A.[Adam], Chen, Y.C.[Yi-Chen], Wang, Z.K.[Ze-Kun], Yang, A.Y.[Allen Y.],
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications,
VDU23(3289-3298)
IEEE DOI 2309
BibRef

Zhang, Z.W.[Zhe-Wen], Wu, F.L.[Fu-Liang], Qiu, Y.M.[Yu-Ming], Liang, J.D.[Jing-Dong], Li, S.W.[Shui-Wang],
Tracking Small and Fast Moving Objects: A Benchmark,
ACCV22(VII:552-569).
Springer DOI 2307
BibRef

Sahin, G.[Gozde], Itti, L.[Laurent],
HOOT: Heavy Occlusions in Object Tracking Benchmark,
WACV23(4819-4828)
IEEE DOI 2302
Training, Visualization, Target tracking, Protocols, Taxonomy, Focusing, Benchmark testing BibRef

Li, J.C.[Jia-Chen], Wang, B.[Bin], Zhu, S.Q.[Shi-Qiang], Cao, X.[Xin], Zhong, F.[Fan], Chen, W.X.[Wen-Xuan], Li, T.[Te], Gu, J.[Jason], Qin, X.Y.[Xue-Ying],
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark,
CVPR22(6687-6696)
IEEE DOI 2210
Deep learning, Training, Uncertainty, Annotations, Shape, Video sequences, Pose estimation and tracking, Datasets and evaluation BibRef

Fan, H.[Heng], Yang, F.[Fan], Chu, P.[Peng], Lin, Y.W.[Yue-Wei], Yuan, L.[Lin], Ling, H.B.[Hai-Bin],
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking,
WACV21(969-978)
IEEE DOI 2106
Measurement, Visualization, Shape, Focusing, Tools, Stability analysis BibRef

Bondi, E., Jain, R., Aggrawal, P., Anand, S., Hannaford, R., Kapoor, A., Piavis, J., Shah, S., Joppa, L., Dilkina, B., Tambe, M.,
BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos,
WACV20(1736-1745)
IEEE DOI 2006
Dataset, Tracking.
WWW Link. Videos, Cameras, Surveillance, Animals, Task analysis, Benchmark testing BibRef

Liu, C.[Chang], Liu, C.L.[Chun-Lei], Yang, L.L.[Lin-Lin], Zhang, B.C.[Bao-Chang],
Tracker Evaluation for Small Object Tracking,
DLPR20(622-629).
Springer DOI 2103
BibRef

Khurana, T.[Tarasha], Dave, A.[Achal], Ramanan, D.[Deva],
Detecting Invisible People,
ICCV21(3154-3164)
IEEE DOI 2203
Measurement, Technological innovation, Solid modeling, Robot vision systems, Object detection, Task analysis, Motion and tracking BibRef

Dave, A.[Achal], Khurana, T.[Tarasha], Tokmakov, P.[Pavel], Schmid, C.[Cordelia], Ramanan, D.[Deva],
TAO: A Large-scale Benchmark for Tracking Any Object,
ECCV20(V:436-454).
Springer DOI 2011
Dataset, Tracking. BibRef

Lukezic, A., Kart, U., Käpylä, J., Durmush, A., Kamarainen, J., Matas, J.G., Kristan, M.,
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark,
ICCV19(10012-10021)
IEEE DOI 2004
Dataset, Tracking. image colour analysis, image sequences, object detection, object tracking, pose estimation, most diverse dataset, Robot sensing systems BibRef

Zheng, H., Yang, J., Chen, J.,
A performance evaluation method for infrared tracker,
IVCNZ17(1-6)
IEEE DOI 1902
image sequences, infrared imaging, performance evaluation, target tracking, video signal processing, image sequence metric, performance evaluation method BibRef

Garon, M.[Mathieu], Laurendeau, D.[Denis], Lalonde, J.F.[Jean-François],
A Framework for Evaluating 6-DOF Object Trackers,
ECCV18(XI: 608-623).
Springer DOI 1810
BibRef

Müller, M.[Matthias], Bibi, A.[Adel], Giancola, S.[Silvio], Alsubaihi, S.[Salman], Ghanem, B.[Bernard],
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild,
ECCV18(I: 310-327).
Springer DOI 1810
Dataset, Tracking. BibRef

Valmadre, J.[Jack], Bertinetto, L.[Luca], Henriques, J.F.[João F.], Tao, R.[Ran], Vedaldi, A.[Andrea], Smeulders, A.W.M.[Arnold W. M.], Torr, P.H.S.[Philip H. S.], Gavves, E.[Efstratios],
Long-Term Tracking in the Wild: A Benchmark,
ECCV18(III: 692-707).
Springer DOI 1810
Dataset, Tracking. BibRef

Sanchez-Matilla, R., Cavallaro, A.,
Confidence Intervals for Tracking Performance Scores,
ICIP18(246-250)
IEEE DOI 1809
Interpolation, Cameras, Tools, Uncertainty, Tracking, Manuals, Object recognition, Tracking performance scores, ground truth, annotation quality BibRef

Karakostas, I.[Iason], Mademlis, I.[Ioannis], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
UAV Cinematography Constraints Imposed by Visual Target Tracking,
ICIP18(76-80)
IEEE DOI 1809
Cameras, Target tracking, Visualization, Cinematography, Drones, UAV cinematography, shot type, target tracking BibRef

Ratnayake, K.[Kumara], Amer, M.A.[Maria A.],
Motion-Augmented Inference and Joint Kernels in Structured Learning for Object Tracking,
ICIAR19(I:45-54).
Springer DOI 1909
BibRef
And:
Drift Detection Using SVM in Structured Object Tracking,
ICIAR19(I:67-76).
Springer DOI 1909
BibRef

Ghoniemy, T., Amer, M.A.,
Optimization Using Artificial Immune Systems Applied To Object Tracking And Segmentation,
ICIP20(2955-2959)
IEEE DOI 2011
Artificial intelligence, Support vector machines, Optimization, Feature extraction, Kernel, Image edge detection, Sociology, object segmentation BibRef

Ghoniemy, T., Valognes, J., Amer, M.A.,
Robust Scoring and Ranking of Object Tracking Techniques,
ICIP18(236-240)
IEEE DOI 1809
Robustness, Dispersion, Video sequences, Object tracking, Object tracking, evaluation, scoring, ranking, robust statistics, outliers BibRef

Khalid, O.[Obaidullah], Cavallaro, A.[Andrea], Rinner, B.[Bernhard],
Detecting tracking errors via forecasting,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Böttger, T., Follmann, P.,
The Benefits of Evaluating Tracker Performance Using Pixel-Wise Segmentations,
VOT17(1983-1991)
IEEE DOI 1802
Benchmark testing, Current measurement, Image segmentation, Optimized production technology, Reliability, Upper bound, Visualization BibRef

Zajc, L.C., Lukežic, A., Leonardis, A., Kristan, M.,
Beyond Standard Benchmarks: Parameterizing Performance Evaluation in Visual Object Tracking,
ICCV17(3343-3351)
IEEE DOI 1802
image motion analysis, object tracking, video signal processing, apparent motion patterns, attribute annotations, Visualization BibRef

Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S.,
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking,
ICCV17(1134-1143)
IEEE DOI 1802
filtering theory, image sequences, object detection, object tracking, video signal processing, Visualization BibRef

Böttger, T.[Tobias], Follmann, P.[Patrick], Fauser, M.[Michael],
Measuring the Accuracy of Object Detectors and Trackers,
GCPR17(415-426).
Springer DOI 1711
More than just the axis aligned box. BibRef

Lehtola, V.[Ville], Huttunen, H.[Heikki], Christophe, F.[Francois], Mikkonen, T.[Tommi],
Evaluation of Visual Tracking Algorithms for Embedded Devices,
SCIA17(I: 88-97).
Springer DOI 1706
BibRef

Walsh, R.[Ryan], Medeiros, H.[Henry],
Detecting Tracking Failures from Correlation Response Maps,
ISVC16(I: 125-135).
Springer DOI 1701
BibRef

Carr, P.[Peter], Collins, R.T.[Robert T.],
Assessing tracking performance in complex scenarios using mean time between failures,
WACV16(1-10)
IEEE DOI 1606
Aggregates BibRef

Maxudov, N.[Nekruzjon], Ercan, A.O.[Ali Ozer], Erdem, A.T.[A. Tanju],
Effect of camera-IMU displacement calibration error on tracking performance,
ICIP15(4476-4480)
IEEE DOI 1512
Camera-IMU displacement calibration BibRef

Nawaz, T., Boyle, J., Li, L.Z.[Long-Zhen], Ferryman, J.M.,
Tracking performance evaluation on PETS 2015 Challenge datasets,
AVSS15(1-6)
IEEE DOI 1511
infrared imaging BibRef

Zhang, G.C.[Guang-Cong], Vela, P.A.[Patricio A.],
Good features to track for visual SLAM,
CVPR15(1373-1382)
IEEE DOI 1510
BibRef

Li, M.Z.[Ming-Zhong], Yin, Z.Z.[Zhao-Zheng],
Debugging Object Tracking Results by a Recommender System with Correction Propagation,
UCCV14(214-228).
Springer DOI 1504
BibRef

Han, B.H.[Bo-Hyung], Hamm, J.[Jihun],
Qualitative Tracking Performance Evaluation without Ground-Truth,
WACV15(55-62)
IEEE DOI 1503
Accuracy BibRef

Zhang, S.[Shu], Staudt, E.[Elliot], Faltemier, T.[Tim], Roy-Chowdhury, A.K.[Amit K.],
A Camera Network Tracking (CamNeT) Dataset and Performance Baseline,
WACV15(365-372)
IEEE DOI 1503
Dataset, Camera Tracking.
WWW Link. Cameras; Legged locomotion; Lighting; Target tracking; Trajectory; Videos BibRef

Comaschi, F.[Francesco], Stuijk, S.[Sander], Basten, T.[Twan], Corporaal, H.[Henk],
A tool for fast ground truth generation for object detection and tracking from video,
ICIP14(368-372)
IEEE DOI 1502
Benchmark testing
See also Online multi-face detection and tracking using detector confidence and structured SVMs. BibRef

Gao, X., Ram, S., Rodriguez, J.J.,
A performance comparison of automatic detection schemes in wide-area aerial imagery,
Southwest16(125-128)
IEEE DOI 1605
Algorithm design and analysis BibRef

Philip, R.C., Ram, S., Gao, X.[Xin], Rodriguez, J.J.,
A comparison of tracking algorithm performance for objects in wide area imagery,
Southwest14(109-112)
IEEE DOI 1406
computer vision BibRef

Pang, Y.[Yu], Ling, H.B.[Hai-Bin],
Finding the Best from the Second Bests: Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms,
ICCV13(2784-2791)
IEEE DOI 1403
BibRef

Song, S.[Shuran], Xiao, J.X.[Jian-Xiong],
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines,
ICCV13(233-240)
IEEE DOI 1403
RGBD; benchmark; tracking BibRef

Abeles, P.[Peter],
Examination of Hybrid Image Feature Trackers,
ISVC13(II:552-561).
Springer DOI 1311
BibRef

Milan, A.[Anton], Schindler, K.[Konrad], Roth, S.[Stefan],
Challenges of Ground Truth Evaluation of Multi-target Tracking,
GT13(735-742)
IEEE DOI 1309
Evaluation;Ground Truth; Multi-object tracking BibRef

Acton, S.T.[Scott T.],
Trackability,
ICIP12(425-428).
IEEE DOI 1302
BibRef

Spampinato, C.[Concetto], Palazzo, S.[Simone], Giordano, D.[Daniela],
Evaluation of tracking algorithm performance without ground-truth data,
ICIP12(1345-1348).
IEEE DOI 1302
BibRef

Matas, J.G.[Jirí G.],
Visual Tracking in the 21st Century,
BMVC12(75).
DOI Link 1301
BibRef

Marcenaro, L.[Lucio], Morerio, P.[Pietro], Regazzoni, C.S.[Carlo S.],
Performance Evaluation of Multi-camera Visual Tracking,
AVSS12(464-469).
IEEE DOI 1211
BibRef

Sankaranarayanan, S.[Swaminathan], Bremond, F.[Francois], Tax, D.M.J.[David M.J.],
Qualitative Evaluation of Detection and Tracking Performance,
AVSS12(362-367).
IEEE DOI 1211
BibRef

Cai, R.T.[Rong-Tai], Wu, Q.X.[Qing-Xiang], Wang, P.[Ping], Zhang, X.G.[Xu-Guang], Hu, S.[Shuo],
Performance analysis of object tracking algorithm,
IASP11(463-467).
IEEE DOI 1112
BibRef

Salvagnini, P.[Pietro], Cristani, M.[Marco], del Bue, A.[Alessio], Murino, V.[Vittorio],
An Experimental Framework for Evaluating PTZ Tracking Algorithms,
CVS11(81-90).
Springer DOI 1109
BibRef

Fang, Y.Q.[Yi-Qiang], Fan, X.[Xiang],
Performance Evaluation for IR Small Target Tracking Algorithm,
ICIG11(749-753).
IEEE DOI 1109
BibRef

Kao, E.K.[Edward K.], Daggett, M.P.[Matthew P.], Hurley, M.B.[Michael B.],
An information theoretic approach for tracker performance evaluation,
ICCV09(1523-1529).
IEEE DOI 0909
BibRef

Stalder, S.[Severin], Grabner, H.[Helmut], Van Gool, L.J.[Luc J.],
Dynamic Objectness for Adaptive Tracking,
ACCV12(III:43-56).
Springer DOI 1304
BibRef
Earlier:
Cascaded Confidence Filtering for Improved Tracking-by-Detection,
ECCV10(I: 369-382).
Springer DOI 1009
BibRef
Earlier:
Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition,
Learning09(1409-1416).
IEEE DOI 0910
BibRef

John, G., Lazarescu, M., West, G.A.W.,
Multi Cue Performance Evaluation Metrics for Tracking in Video Sequences,
DICTA08(257-264).
IEEE DOI 0812
BibRef

Perera, A.G.A., Hoogs, A.J., Srinivas, C., Brooksby, G., Hu, W.[Wensheng],
Evaluation of Algorithms for Tracking Multiple Objects in Video,
AIPR06(35-35).
IEEE DOI 0610
BibRef

Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Group Segmentation During Object Tracking Using Optical Flow Discontinuities,
PSIVT10(270-275).
IEEE DOI 1011

See also Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking. BibRef

Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha], Ryan, D.,
Multi-Modal Object Tracking using Dynamic Performance Metrics,
AVSS10(286-293).
IEEE DOI 1009

See also Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking. BibRef

Fernando, T.[Tharindu], Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds,
ACCV18(I:314-330).
Springer DOI 1906
BibRef
Earlier:
Tracking by Prediction: A Deep Generative Model for Mutli-person Localisation and Tracking,
WACV18(1122-1132)
IEEE DOI 1806
feature extraction, learning (artificial intelligence), neural nets, sensor fusion, target tracking, appearance models, Trajectory BibRef

Moudgil, A.[Abhinav], Gandhi, V.[Vineet],
Long-Term Visual Object Tracking Benchmark,
ACCV18(II:629-645).
Springer DOI 1906
BibRef

Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha], Lakemond, R.[Ruan],
Dynamic Performance Measures for Object Tracking Systems,
AVSBS09(541-546).
IEEE DOI 0909
BibRef

Tron, R.[Roberto], Vidal, R.[Rene],
A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms,
CVPR07(1-8).
IEEE DOI
PDF File. 0706

See also Hopkins 155. BibRef

Mikram, M.[Mounia], Mégret, R.[Rémi], Berthoumieu, Y.[Yannick],
Evaluating Descriptors Performances for Object Tracking on Natural Video Data,
ACIVS07(352-363).
Springer DOI 0708
BibRef

Skoglund, J.[Johan], Felsberg, M.[Michael],
Evaluation of Subpixel Tracking Algorithms,
ISVC06(II: 374-382).
Springer DOI 0611
BibRef

Jaynes, C., Kale, A., Sanders, N., Grossmann, E.,
The Terrascope Dataset: Scripted Multi-Camera Indoor Video Surveillance with Ground-truth,
PETS05(309-316).
IEEE DOI
WWW Link. 0602
Dataset, Surveillance. BibRef

Needham, C.J.[Chris J.], Boyle, R.D.[Roger D.],
Performance Evaluation Metrics and Statistics for Positional Tracker Evaluation,
CVS03(278 ff).
Springer DOI 0306
BibRef

Robles, V.[Vanessa], Alegre, E.[Enrique], Sebastian, J.M.[Jose M.],
Tracking Algorithms Evaluation in Feature Points Image Sequences,
ICIAR04(II: 589-596).
Springer DOI 0409
BibRef

Tomasi, C., Shi, J.,
Good Features to Track,
CVPR94(593-600).
IEEE DOI Features, Evaluation. Select features based on how the tracker works.
See also Shape and Motion from Image Streams: A Factorization Method Part 3 - Detection and Tracking of Point Features. BibRef 9400

Marshall, W.C.[William C.],
Evaluation of Image Tracker Algorithms,
SPIE(1483), 1991, pp. 207-218. BibRef 9100

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Target Tracking Challenges, Result Summaries .