Tracking Methods in Sports: a Review of Advances, Quality, and
                    Challenges in Performance Data

Felipe Arruda Moura; Fabio Giuliano Caetano; Ricardo da Silva Torres

doi:10.1055/a-2577-2739

International Journal of Sports Medicine, Inhaltsverzeichnis

Int J Sports Med 2025; 46(09): 621-660
DOI: 10.1055/a-2577-2739

Review

Tracking Methods in Sports: a Review of Advances, Quality, and Challenges in Performance Data

Felipe Arruda Moura

¹Sport Sciences Department, State University of Londrina, Londrina, Brazil (Ringgold ID: RIN37894)

,

Fabio Giuliano Caetano

¹Sport Sciences Department, State University of Londrina, Londrina, Brazil (Ringgold ID: RIN37894)

,

Ricardo da Silva Torres

²Artificial Intelligence Group (AIN), Wageningen University & Research, Wageningen, Netherlands (Ringgold ID: RIN4508)

› Institutsangaben

Abstract

Tracking systems in sports aim to record the athlete’s position as a function of time. From these data, information on physical, tactical and technical performance is obtained and assists coaches and players in decision-making during the training and competition routine. The implementation, feasibility, and quality of data generated by tracking systems depend on the conditions of each sporting environment and their requirements. This narrative review addresses the fundamentals of the main tracking systems, including algorithms based on computer vision and artificial intelligence for processing videos and global (global positioning system and global navigation satellite system) and local positioning systems. We also address technological advances for obtaining data from human pose estimation and the main validation or quality analysis studies of each method. Finally, we present a series of recommendations and future directions for the evaluation and development of automatic and accurate athlete tracking tools.

Keywords

electronic performance and tracking system - global positioning system - global navigation satellite system - human pose estimation - multiple people tracking - artificial intelligence

Volltext

Referenzen

References
1 Brocherie F, Beard A. All Alone We Go Faster, Together We Go Further: The Necessary Evolution of Professional and Elite Sporting Environment to Bridge the Gap Between Research and Practice. Front Sports Act Living 2020; 2: 631147
2 Reilly T, Thomas V. A motion analysis work-rate in different positional roles in professional football match-play. J Hum Mov Stud 1976; 2: 87-97
3 Withers RT, Maricic Z, Wasilewski S. et al. Match analyses of Australian professional soccer players. J Hum Mov Stud 1982; 8: S.159-176
4 Teixeira JE, Forte P, Ferraz R. et al. Integrating physical and tactical factors in football using positional data: A systematic review. PeerJ 2022; 10: e14381
5 Hausler J, Halaki M, Orr R. Application of Global Positioning System and Microsensor Technology in Competitive Rugby League Match-Play: A Systematic Review and Meta-analysis. Sports Med (Auckland, NZ) 2016; 46: 559-588
6 Olthof SBH, Frencken WGP, Lemmink K. Match-derived relative pitch area changes the physical and team tactical performance of elite soccer players in small-sided soccer games. J Sports Sci 2018; 36: 1557-1563
7 Rico-González M, Pino-Ortega J, Clemente FM. et al. A systematic review of collective tactical behaviour in futsal using positional data. Biol Sport 2021; 38: 23-36
8 Ohashi J, Togari H, Isokawa M. et al. Measuring movement speeds and distances covered during soccer match-play. In: Reilly T, Lees A, Davids K et al., eds. Science and football: Proceedings of the first World Congress of Science and Football. London: E. & F.N. Spon; 1988. pp. 449-455
9 Barros RM, Misuta MS, Menezes RP. et al. Analysis of the distances covered by first division Brazilian soccer players obtained with an automatic tracking method. J Sports Sci Med 2007; 6: 233-242
10 Aughey RJ, Ball K, Robertson SJ. et al. Comparison of a computer vision system against three-dimensional motion capture for tracking football movements in a stadium environment. Sports Eng 2022; 25: 2
11 De Oliveira Bueno MJ, Caetano FG, Pereira TJC. et al. Analysis of the distance covered by Brazilian professional futsal players during official matches. Sports Biomech 2014; 13: 230-240
12 Hennig EM, Briehle R. Game analysis by GPS satellite tracking of soccer players. XI Congress of the Canadian Society for Biomechanics. Montreal, Canada. 2000 p. 44.
13 FIFA. Approval of Electronic Performance and Tracking System (EPTS) devices 2015 https://digitalhub.fifa.com/m/2b487046465fa949/original/x4fy2rzjzaotp6usjtyk-pdf.pdf [accessed Jan. 9, 2025]
14 Scott MT, Scott TJ, Kelly VG. The Validity and Reliability of Global Positioning Systems in Team Sport: A Brief Review. J Strength Cond Res 2016; 30: 1470-1490
15 Frencken WGP, Lemmink KAPM, Delleman NJ. Soccer-specific accuracy and validity of the local position measurement (LPM) system. J Sci Med Sport 2010; 13: 641-645
16 Ogris G, Leser R, Horsak B. et al. Accuracy of the LPM tracking system considering dynamic position changes. J Sports Sci 2012; 30: 1503-1511
17 Ludwig K, Einfalt M, Lienhart R. Robust Estimation of Flight Parameters for SKI Jumpers. 2020 IEEE Int Conf on Multimedia & Expo Workshops (ICMEW). 2020 pp. 1-6
18 Banks LS, Santiago PRP, da Silva Torres R. et al. Accuracy of a markerless system to estimate the position of taekwondo athletes in an official combat area. Int J Perf Anal Sport 2024; 24: 1-16
19 Murrugarra-Llerena J, Kirsten L, Jung CR. Can we trust bounding box annotations for object detection?. 2022 IEEE/CVF Conf Comput Vis Patt Recog Workshops (CVPRW). 2022 pp. 4812-4821
20 Zhong B, Wu H, Ding L. et al. Mapping computer vision research in construction: Developments, knowledge gaps and implications for research. Autom Constr 2019; 107: 102919
21 Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science 2021; 2: 420
22 Liu L, Ouyang W, Wang X. et al. Deep Learning for Generic Object Detection: A Survey. Int J Comp Vision 2020; 128: 261-318
23 Samkari E, Arif M, Alghamdi M. et al. Human Pose Estimation Using Deep Learning: A Systematic Literature Review. Mach Learn Knowl Extr 2023; 4: 1612-1659
24 Yu Y, Wang C, Fu Q. et al. Techniques and Challenges of Image Segmentation: A Review. Electronics 2023; 12: 1199
25 Robertson DGE, Caldwell GE, Hamill J. et al. Research methods in biomechanics. 2nd edn. Champaign, Illinois: Human Kinetics; 2014
26 Latzel R, Glauner P. Artificial Intelligence in Sport Scientific Creation and Writing Process. In: Dindorf C, Bartaguiz E, Gassmann F et al., eds. Artificial Intelligence in Sports, Movement, and Health. Cham: Springer Nature Switzerland; 2024. pp. 15-29
27 Luo W, Xing J, Milan A. et al. Multiple object tracking: A literature review. Artif Intell 2021; 293: 103448
28 Preim B, Botha C. Chapter 18 – Image-Guided Surgery and Augmented Reality. In: Preim B, Botha C, eds. Visual Computing for Medicine. 2nd edn). Boston: Morgan Kaufmann; 2014. pp. 625-663
29 Manafifard M, Ebadi H, Abrishami Moghaddam H. A survey on player tracking in soccer videos. Comput Vis Image Und 2017; 159: 19-46
30 Yilmaz A, Javed O, Shah M. Object tracking: A survey. ACM Comput Surveys (CSUR) 2006; 38: 13–es
31 Yang H, Shao L, Zheng F. et al. Recent advances and trends in visual tracking: A review. Neurocomputing 2011; 74: 3823-3831
32 Dollar P, Wojek C, Schiele B. et al. Pedestrian Detection: An Evaluation of the State of the Art. IEEE Trans Pattern Anal Mach Intell 2012; 34: 743-761
33 Zafeiriou S, Zhang C, Zhang Z. A survey on face detection in the wild: Past, present and future. Comput Vis Image Und 2015; 138: 1-24
34 Barris S, Button C. A Review of Vision-Based Motion Analysis in Sport. Sports Med 2008; 38: 1025-1043
35 Liu J, Huang G, Hyyppä J. et al. A survey on location and motion tracking technologies, methodologies and applications in precision sports. Expert Syst Appl 2023; 229: 120492
36 Liu J, Tong X, Li W. et al. Automatic player detection, labeling and tracking in broadcast soccer video. Pattern Recognit Lett 2009; 30: 103-113
37 Beetz M, Gedikli S, Bandouch J. et al Visually Tracking Football Games Based on TV Broadcasts. International Joint Conference on Artificial Intelligence (IJCAI) Vol. 7 2007; pp. 2066-2071
38 Moura FA, Martins LEB, Anido RO. et al. Quantitative analysis of Brazilian football players’ organisation on the pitch. Sports Biomech 2012; 11: 85-96
39 Rico-Gonzalez M, Ortega JP, Nakamura FY. et al. Identification, Computational Examination, Critical Assessment and Future Considerations of Spatial Tactical Variables to Assess the Use of Space in Team Sports by Positional Data: A Systematic Review. J Hum Kinet 2021; 77: 205-221
40 Allen T, Taberner M, Zhilkin M. et al. Running more than before? The evolution of running load demands in the English Premier League. Int J Sports Sci Coach 2024; 19: 779-787
41 Vieira LHP, Pagnoca EA, Milioni F. et al. Tracking futsal players with a wide-angle lens camera: accuracy analysis of the radial distortion correction based on an improved Hough transform algorithm. Comput Methods Biomech Biomed Eng 2017; 5: 221-231
42 Michalsik LB, Madsen K, Aagaard P. Match Performance and Physiological Capacity of Female Elite Team Handball Players. Int J Sports Med 2014; 35: 595-607
43 Barbero-Alvarez JC, Soto VM, Barbero-Alvarez V. et al. Match analysis and heart rate of futsal players during competition. J Sports Sci 2008; 26: 63-73
44 Bueno MJO, Caetano FG, Pereira TJ. et al. Analysis of the distance covered by Brazilian professional futsal players during official matches. Sports Biomech 2014; 13: 230-240
45 Fonseca S, Milho J, Travassos B. et al. Spatial dynamics of team sports exposed by Voronoi diagrams. Hum Mov Sci 2012; 31: 1652-1659
46 Pereira TJC, Nakamura FY, de Jesus MT. et al. Analysis of the distances covered and technical actions performed by professional tennis players during official matches. J Sports Sci 2017; 35: 361-368
47 Sarro KJ, Misuta MS, Burkett B. et al. Tracking of wheelchair rugby players in the 2008 Demolition Derby final. J Sports Sci 2010; 28: 193-200
48 Perš J, Bon M, Kovačič S. et al. Observation and analysis of large-scale human motion. Hum Mov Sci 2002; 21: 295-311
49 Scanlan AT, Dascombe BJ, Reaburn P. et al. The physiological and activity demands experienced by Australian female basketball players during competition. J Sci Med Sport 2012; 15: 341-347
50 Barros RML, Menezes RP, Russomanno TG. et al. Measuring handball players trajectories using an automatically trained boosting algorithm. Comput Methods Biomech Biomed Eng 2011; 14: 53-63
51 Okuma K, Taleghani A, de Freitas N. et al. A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla T, Matas J eds, Computer Vision – ECCV 2004. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. pp. 28-39
52 Xing J, Ai H, Liu L. et al. Multiple Player Tracking in Sports Video: A Dual-Mode Two-Way Bayesian Inference Approach With Progressive Observation Modeling. IEEE Trans Image Proc 2011; 20: 1652-1667
53 Kasiri-Bidhendi S, Safabakhsh R. Effective tracking of the players and ball in indoor soccer games in the presence of occlusion. 2009 14th International CSI Computer Conference 2009; pp. 524-529
54 Figueroa PJ, Leite NJ, Barros RML. Tracking soccer players aiming their kinematical motion analysis. Comput Vis Image Underst 2006; 101: 122-135
55 Gomez G, Herrera López P, Link D. et al. Tracking of Ball and Players in Beach Volleyball Videos. PLoS One 2014; 9: e111730
56 Yamamoto T, Kataoka H, Hayashi M. et al Multiple players tracking and identification using group detection and player number recognition in sports video. IECON 2013 – 39th Annual Conference of the IEEE Industrial Electronics Society 2013; pp. 2442-2446
57 Ibraheem OW, Irwansyah A, Hagemeyer J. et al. Reconfigurable vision processing system for player tracking in indoor sports. 2017 Conference on Design and Architectures for Signal and Image Processing (DASIP). 2017 pp. 1-6
58 Huang S, Zhuang X, Ikoma N. et al Particle filter with least square fitting prediction and spatial relationship based multi-view elimination for 3D Volleyball players tracking. 2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA) 2016; pp. 28-31
59 Lara JPR, Vieira CLR, Misuta MS. et al. Validation of a video-based system for automatic tracking of tennis players. Int J Perform Anal Sport 2018; 18: 137-150
60 Chiari L, Della Croce U, Leardini A. et al. Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. Gait Posture 2005; 21: 197-211
61 Vučković G, Perš J, James N. et al. Measurement error associated with the SAGIT/Squash computer tracking software. Eur J Sport Sci 2010; 10: 129-140
62 Valter DS, Adam C, Barry M. et al. Validation of Prozone ®: A new video-based performance analysis system. Int J Perf Anal Sport 2006; 6: 108-119
63 Buchheit M, Allen A, Poon TK. et al. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sports Sci 2014; 32: 1844-1857
64 Linke D, Link D, Lames M. Validation of electronic performance and tracking systems EPTS under field conditions. PLoS One 2018; 13: e0199519
65 Linke D, Link D, Lames M. Football-specific validity of TRACAB’s optical video tracking systems. PLoS One 2020; 15: e0230179
66 Redwood-Brown A, Cranton W, Sunderland C. Validation of a real-time video analysis system for soccer. Int J Sports Med 2012; 33: 635-640
67 Mara J, Morgan S, Pumpa K. et al. The Accuracy and Reliability of a New Optical Player Tracking System for Measuring Displacement of Soccer Players. Int J Comput Sci Sport 2017; 16: 175-184
68 Siegle M, Stevens T, Lames M. Design of an accuracy study for position detection in football. J Sports Sci 2013; 31: 166-172
69 Lara JPR, Vieira CLR, Misuta MS. et al. Validation of a video-based system for automatic tracking of tennis players. Int J Perf Anal Sport 2018; 18: 137-150
70 Palut Y, Zanone P-G. A dynamical analysis of tennis: Concepts and data. J Sports Sci 2005; 23: 1021-1032
71 Passos P, Araújo D, Davids K. et al. Interpersonal dynamics in sport: The role of artificial neural networks and 3-D analysis. Behav Res Methods 2006; 38: 683-691
72 Link D, Weber M, Linke D. et al. Can Positioning Systems Replace Timing Gates for Measuring Sprint Time in Ice Hockey. Front Physiol 2019; 9: 1-10
73 GPS.gov. Space Segment. 2024 https://www.gps.gov/systems/gps/space/ [accessed June 26, 2024]
74 Jackson BM, Polglaze T, Dawson B. et al. Comparing Global Positioning System and Global Navigation Satellite System Measures of Team-Sport Movements. Int J Sports Physiol Perform 2018; 13: 1005-1010
75 World G. The Almanac – GNSS Constellations Summary. 2024 https://www.gpsworld.com/the-almanac-gnss-constellations-summary/ [accessed June 26, 2024]
76 Yang J, Zhang Z, Liu Y. et al. Research on time difference detection algorithm based on combination of GNSS and PPP. EURASIP J Wirel Commun Netw 2019; 2019: 114
77 Adriano Pereira L, Freitas V, Arruda Moura F. et al. The Activity Profile of Young Tennis Athletes Playing on Clay and Hard Courts: Preliminary Data. J Hum Kinet 2016; 50: 211-218
78 Wundersitz DWT, Gastin PB, Robertson S. et al. Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements. Int J Sports Med 2015; 36: 742-746
79 Liu T-H, Chen W-H, Shih Y. et al. Better position for the wearable sensor to monitor badminton sport training loads. Sports Biomech 2024; 23: 503-515
80 Simons C, Bradshaw EJ. Reliability of accelerometry to assess impact loads of jumping and landing tasks. Sports Biomech 2016; 15: 1-10
81 Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform 2011; 6: 311-321
82 Edwards S, White S, Humphreys S. et al. Caution using data from triaxial accelerometers housed in player tracking units during running. J Sports Sci 2019; 37: 810-818
83 Alexander JP, Hopkinson TL, Wundersitz DW. et al. Validity of a Wearable Accelerometer Device to Measure Average Acceleration Values During High-Speed Running. J Strength Cond Res 2016; 30: 3007-3013
84 Roell M, Mahler H, Lienhard J. et al. Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments. Sensors 2019; 19 (16) 3458
85 Gómez-Carmona CD, Bastida-Castillo A, Ibáñez SJ. et al. Accelerometry as a method for external workload monitoring in invasion team sports. A systematic review. PLoS One 2020; 15: e0236643
86 Malone JJ, Lovell R, Varley MC. et al. Unpacking the Black Box: Applications and Considerations for Using GPS Devices in Sport. Int J Sports Physiol Perform 2017; 12: S2-18-S12-26
87 Nagahara R, Botter A, Rejc E. et al. Concurrent Validity of GPS for Deriving Mechanical Properties of Sprint Acceleration. Int J Sports Physiol Perform 2017; 12: 129-132
88 Aughey RJ. Applications of GPS Technologies to Field Sports. Int J Sports Physiol Perform 2011; 6: 295-310
89 Johnston RJ, Watsford ML, Kelly SJ. et al. Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands. J Strength Cond Res 2014; 28: 1649-1655
90 Padulo J, Iuliano E, Brisola G. et al. Validity and reliability of a standalone low-end 50-Hz GNSS receiver during running. Biol Sport 2019; 36: 75-80
91 Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport 2010; 13: 133-135
92 Rampinini E, Alberti G, Fiorenza M. et al. Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int J Sports Med 2015; 36: 49-53
93 Gilgien M, Spörri J, Limpach P. et al. The Effect of Different Global Navigation Satellite System Methods on Positioning Accuracy in Elite Alpine Skiing. Sensors (Basel) 2014; 14: 18433-18453
94 Witte TH, Wilson AM. Accuracy of non-differential GPS for the determination of speed over ground. J Biomechanics 2004; 37: 1891-1898
95 Kaplan ED, Hegarty C. Understanding GPS/GNSS: principles and applications. 3rd edn. Boston, London: Artech House; 2017
96 Kapteijns JA, Caen K, Lievens M. et al. Positional Match Running Performance and Performance Profiles of Elite Female Field Hockey. Int J Sports Physiol Perform 2021; 16: 1295-1302
97 Gualtieri A, Rampinini E, Dello Iacono A. et al. High-speed running and sprinting in professional adult soccer: Current thresholds definition, match demands and training strategies. A systematic review. Front Sports Act Living 2023; 5: 1116293
98 Townshend AD, Worringham CJ, Stewart IB. Assessment of speed and position during human locomotion using nondifferential GPS. Med Sci Sports Exerc 2008; 40: 124-132
99 Crang ZL, Duthie G, Cole MH. et al. The Validity and Reliability of Wearable Microtechnology for Intermittent Team Sports: A Systematic Review. Sports Med (Auckland, NZ) 2021; 51: 549-565
100 Ferraz A, Duarte-Mendes P, Sarmento H. et al. Tracking devices and physical performance analysis in team sports: a comprehensive framework for research—trends and future directions. Front Sports Act Living 2023; 23: 5
101 Torres-Ronda L, Beanland E, Whitehead S. et al. Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis. Sports Med Open 2022; 8: 15
102 Castellano J, Casamichana D. Heart Rate and Motion Analysis by GPS in Beach Soccer. J Sports Sci Med 2010; 9: 98-103
103 Barr M, Beaver T, Turczyn D. et al. Validity and Reliability of 15 Hz Global Positioning System Units for Assessing the Activity Profiles of University Football Players. J Strength Cond Res 2019; 33: 1371-1379
104 Aughey RJ, Falloon C. Real-time versus post-game GPS data in team sports. J Sci Med Sport 2010; 13: 348-349
105 Kunisada Y, Premachandra C. High Precision Location Estimation in Mountainous Areas Using GPS. Sensors (Basel) 2022; 22: 1149
106 Gløersen Ø, Kocbach J, Gilgien M. Tracking Performance in Endurance Racing Sports: Evaluation of the Accuracy Offered by Three Commercial GNSS Receivers Aimed at the Sports Market. Front Physiol 2018; 9: 1425
107 Sakurai Y, Fujita Z, Ishige Y. Automated identification and evaluation of subtechniques in classical-style roller skiing. J Sports Sci Med 2014; 13: 651-657
108 Gilgien M, Spörri J, Chardonnens J. et al. Determination of the centre of mass kinematics in alpine skiing using differential global navigation satellite systems. J Sports Sci 2015; 33: 960-969
109 Best R, Standing R. Feasibility of a Global Positioning System to Assess the Spatiotemporal Characteristics of Polo Performance. J Equine Vet Sci 2019; 79: 59-62
110 Bastida Castillo A, Gómez Carmona CD, De la cruz sánchez E. et al. Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time-motion analyses in soccer. Eur J Sport Sci 2018; 18: 450-457
111 Bastida-Castillo A, Gómez-Carmona CD, De La Cruz Sánchez E. et al. Comparing accuracy between global positioning systems and ultra-wideband-based position tracking systems used for tactical analyses in soccer. Eur J Sport Sci 2019; 19: 1157-1165
112 Kim M, Park C, Yoon J. The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players. Sensors 2023; 23: 1749
113 Nikolaidis PT, Clemente FM, van der Linden CMI. et al. Validity and Reliability of 10-Hz Global Positioning System to Assess In-line Movement and Change of Direction. Front Physiol 2018; 9: 228
114 Lacome M, Peeters A, Mathieu B. et al. Can we use GPS for assessing sprinting performance in rugby sevens? A concurrent validity and between-device reliability study. Biol Sport 2019; 36: 25-29
115 Roe G, Darrall-Jones J, Black C. et al. Validity of 10-HZ GPS and Timing Gates for Assessing Maximum Velocity in Professional Rugby Union Players. Int J Sports Physiol Perform 2017; 12: 836-839
116 Fischer-Sonderegger K, Taube W, Rumo M. et al. How far from the gold standard? Comparing the accuracy of a Local Position Measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports. PLoS One 2021; 16: e0250549
117 Hoppe MW, Baumgart C, Polglaze T. et al. Validity and reliability of GPS and LPS for measuring distances covered and sprint mechanical properties in team sports. PLoS One 2018; 13: e0192708
118 Bataller-Cervero AV, Gutierrez H, DeRentería J. et al. Validity and Reliability of a 10 Hz GPS for Assessing Variable and Mean Running Speed. J Hum Kinet 2019; 67: 17-24
119 Muñoz-López A, Granero-Gil P, Pino-Ortega J. et al. The validity and reliability of a 5-hz GPS device for quantifying athletes’ sprints and movement demands specific to team sports. J Human Sport Exerc 2017; 12: 156-166
120 Beato M, Coratella G, Stiff A. et al. The Validity and Between-Unit Variability of GNSS Units (STATSports Apex 10 and 18 Hz) for Measuring Distance and Peak Speed in Team Sports. Front Physiol 2018; 9: 1288
121 Delaney JA, Wileman TM, Perry NJ. et al. The Validity of a Global Navigation Satellite System for Quantifying Small-Area Team-Sport Movements. J Strength Cond Res 2019; 33: 1463-1466
122 Beato M, Coratella G, Stiff A. et al. The Validity and Between-Unit Variability of GNSS Units (STATSports Apex 10 and 18 Hz) for Measuring Distance and Peak Speed in Team Sports. Front Physiol 2018; 9: 1288
123 Vickery WM, Dascombe BJ, Baker JD. et al. Accuracy and reliability of GPS devices for measurement of sports-specific movement patterns related to cricket, tennis, and field-based team sports. J Strength Cond Res 2014; 28: 1697-1705
124 Rawstorn JC, Maddison R, Ali A. et al. Rapid directional change degrades GPS distance measurement validity during intermittent intensity running. PLoS One 2014; 9: e93693
125 Willmott AGB, James CA, Bliss A. et al. A comparison of two global positioning system devices for team-sport running protocols. J Biomech 2019; 83: 324-328
126 Fernandes RA, Alacid F, Gomes AB. et al. Validation of a global positioning system with accelerometer for canoe/kayak sprint kinematic analysis. Sports Biomech 2024; 23: 1-12
127 Mejuto G, Gómez-Carmona CD, Gracia J. et al. Surfing Time–Motion Characteristics Possible to Gain Using Global Navigation Satellite Systems: A Systematic Review. Sensors 2024; 24 (11) 3455
128 Staunton CA, Colyer SL, Karlsson Ø. et al. Performance and Micro-Pacing Strategies in a Freestyle Cross-Country Skiing Distance Race. Front Sports Act Living 2022; 4: 834474
129 Alejo LB, Gil-Cabrera J, Montalvo-Pérez A. et al. Performance Parameters in Competitive Alpine Skiing Disciplines of Slalom, Giant Slalom and Super-Giant Slalom. Int J Environ Res Public Health 2021; 18: 2628
130 Baumgart JK, Haugnes P, Bardal LM. et al. Development of a Framework for the Investigation of Speed, Power, and Kinematic Patterns in Para Cross-Country Sit-Skiing: A Case Study of an LW12 Athlete. 2019; 1
131 Pfau T, Bruce OL, Sawatsky A. et al. Dirt Track Surface Preparation and Associated Differences in Speed, Stride Length, and Stride Frequency in Galloping Horses. Sensors (Basel) 2024; 24: 2441
132 Caraballo I, Pezelj L, Ramos-Álvarez JJ. Analysis of the Performance and Sailing Variables of the Optimist Class in a Variety of Wind Conditions. J Funct Morphol Kinesiol 2024; 9: 18
133 Furr HN, Nessler JA, Newcomer SC. Characterization of Heart Rate Responses, Duration, and Distances Traveled in Youth Participating in Recreational Skateboarding at Community Skateparks. J Strength Cond Res 2021; 35: 542-548
134 Martin L, Lambeth-Mansell A, Beretta-Azevedo L. et al. Even between-lap pacing despite high within-lap variation during mountain biking. Int J Sports Physiol Perform 2012; 7: 261-270
135 Aoyagi A, Ishikura K, Nabekura Y. Exercise Intensity during Olympic-Distance Triathlon in Well-Trained Age-Group Athletes: An Observational Study. Sports (Basel) 2021; 9: 18
136 Stelzer A, Pourvoyeur K, Fischer A. Concept and application of LPM – a novel 3-D local position measurement system. IEEE Trans Microw Theory Tech 2004; 52: 2664-2669
137 Blauberger P, Marzilger R, Lames M. Validation of Player and Ball Tracking with a Local Positioning System. Sensors 2021; 21: 1-13
138 Leser R, Schleindlhuber A, Lyons K. et al. Accuracy of an UWB-based position tracking system used for time-motion analyses in game sports. Eur J Sport Sci 2014; 14: 635-642
139 Alt PS, Baumgart C, Ueberschär O. et al. Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports. Sensors 2020; 20: 1-10
140 Bischofberger J, Baca A, Schikuta E. Event detection in football: Improving the reliability of match analysis. PLoS One 2024; 19: e0298107
141 Khaustov V, Mozgovoy M. Recognizing Events in Spatiotemporal Soccer Data. PLoS One 2024; 19: e0298107
142 Richly K, Moritz F, Schwarz C. Utilizing Artificial Neural Networks to Detect Compound Events in Spatio-Temporal Soccer Data. 3rd SIGKDD Workshop on Mining and Learning from Time Series. 2017
143 Stevens TGA, de Ruiter CJ, van Niel C. et al. Measuring Acceleration and Deceleration in Soccer-Specific Movements Using a Local Position Measurement (LPM) System. Int J Sports Physiol Perform 2014; 9: 446-456
144 Conners RT, Whitehead PN, Dodds FT. et al. Validation of the Polar Team Pro System for Sprint Speed With Ice Hockey Players. J Strength Cond Res 2022; 36: 3468-3472
145 Link D, Weber M, Linke D. et al. Can Positioning Systems Replace Timing Gates for Measuring Sprint Time in Ice Hockey?. Front Physiol 2019; 9: 1882
146 Figueira B, Gonçalves B, Folgado H. et al. Accuracy of a Basketball Indoor Tracking System Based on Standard Bluetooth Low Energy Channels (NBN23®). Sensors 2018; 18 (06) 1-8
147 Pricone M, Caracaş A. A heterogeneous RSSI-based localization system for indoor and outdoor sports activities. 2014 Int Wireless Commun Mobile Comput Conf (IWCMC). 2014 pp. 274-280
148 Luteberget LS, Spencer M, Gilgien M. Validity of the Catapult ClearSky T6 Local Positioning System for Team Sports Specific Drills, in Indoor Conditions. Front Physiol 2018; 9: 115
149 Fleureau A, Lacome M, Buchheit M. et al. Validity of an ultra-wideband local positioning system to assess specific movements in handball. Biol Sport 2020; 37: 351-357
150 Serpiello FR, Hopkins WG, Barnes S. et al. Validity of an ultra-wideband local positioning system to measure locomotion in indoor sports. J Sports Sci 2018; 36: 1727-1733
151 Pino-Ortega J, Bastida-Castillo A, Gómez-Carmona CD. et al. Validity and reliability of an eight antennae ultra-wideband local positioning system to measure performance in an indoor environment. Sports Biomech 2024; 23: 145-155
152 Sathyan T, Shuttleworth R, Hedley M. et al. Validity and reliability of a radio positioning system for tracking athletes in indoor and outdoor team sports. Behav Res Methods 2012; 44: 1108-1114
153 Rhodes J, Mason B, Perrat B. et al. The validity and reliability of a novel indoor player tracking system for use within wheelchair court sports. J Sports Sci 2014; 32: 1639-1647
154 Hodder RW, Ball KA, Serpiello FR. Criterion Validity of Catapult ClearSky T6 Local Positioning System for Measuring Inter-Unit Distance. Sensors 2020; 20 (13) 3693
155 Van der Slikke RMA, Sindall P, Goosey-Tolfrey VL. et al. Load and performance monitoring in wheelchair court sports: A narrative review of the use of technology and practical recommendations. Eur J Sport Sci 2023; 23: 189-200
156 Padilla R, Netto SL, Silva EAbD. A Survey on Performance Metrics for Object-Detection Algorithms. 2020 Int Conf Syst Signals Image Proc (IWSSIP) 2020; pp. 237-242
157 Cao W, Wang X, Liu X. et al. A deep learning framework for multi-object tracking in team sports videos. IET Comput Vision 2024; 18: 1-17
158 Hicks SA, Strümke I, Thambawita V. et al. On evaluation metrics for medical applications of artificial intelligence. Sci Rep 2022; 12: 5979
159 Leal-Taixé L, Milan A, Reid I. et al. MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking. arXiv:150401942 1-15
160 Lin T-Y, Maire M, Belongie S. et al. Microsoft COCO: Common Objects in Context. arXiv e-prints 2014; arXiv:1405.0312
161 Russakovsky O, Deng J, Su H. et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 2015; 115: 211-252
162 Sun P, Cao J, Jiang Y. et al. DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion. arXiv e-prints 2021; arXiv:2111.14690
163 Lin W, Liu H, Liu S. et al. Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events. arXiv e-prints 2020; arXiv:2005.04490
164 Cui Y, Zeng C, Zhao X. et al. SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes. arXiv e-prints 2023; arXiv:2304.05170
165 Wang A, Chen H, Liu L. et al. YOLOv10: Real-Time End-to-End Object Detection. arXiv e-prints 2024; arXiv:2405.14458
166 Wang C-Y, Yeh IH, Liao H-YM. YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv e-prints 2024; arXiv:2402.13616
167 Zhang Y, Wang C, Wang X. et al. FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking. Int J Comput Vis 2021; 129: 3069-3087
168 Voigtlaender P, Krause M, Osep A. et al MOTS: Multi-Object Tracking and Segmentation. 2019 IEEE/CVF Conf Comput Vis Patt Recog (CVPR) 2019; pp. 7934-7943
169 Milan A, Leal-Taixe L, Reid I. et al. MOT16: A Benchmark for Multi-Object Tracking. arXiv e-prints 2016; arXiv:1603.00831
170 Jiang X, Li J, Jia H. et al Improved FairMOT for multi-pedestrian tracking in complex environments. 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) 2024; pp. 287-291
171 Brown RG, Hwang PYC. Introduction to random signals and applied Kalman filtering: with MATLAB exercises. 4th edn. Hoboken, NJ: John Wiley; 2012
172 Aharon N, Orfaig R, Bobrovsky B-Z. BoT-SORT: Robust Associations Multi-Pedestrian Tracking. arXiv e-prints 2022; arXiv:2206.14651
173 Jung H, Kang S, Kim T. et al ConfTrack: Kalman Filter-based Multi-Person Tracking by Utilizing Confidence Score of Detection Box. 2024 IEEE/CVF Winter Conf Appl Comput Vis (WACV) 2024; pp. 6569-6578
174 Dendorfer P, Rezatofighi H, Milan A. et al. MOT20: A benchmark for multi object tracking in crowded scenes. arXiv e-prints 2020; arXiv:2003.09003
175 Dendorfer P, Rezatofighi H, Milan A. et al. MOT20: Pedestrian Detection Challenge. 2024 https://motchallenge.net/results/MOT20/ [accessed May 17, 2024]
176 FIFA. Semi-automated offside technology. 2023 https://inside.fifa.com/technical/football-technology/football-technologies-and-innovations-at-the-fifa-world-cup-2022/semi-automated-offside-technology [accessed July 17, 2024]
177 Jin S, Ma X, Han Z. et al Towards Multi-Person Pose Tracking : Bottom-up and Top-down Methods. ICCV PoseTrack Workshop 2017; pp. 1-4
178 Fu Z, Zuo W, Hu Z. et al. Improving Multi-Person Pose Tracking with A Confidence Network. arXiv e-prints 2023; arXiv:2310.18920
179 Cao Z, Simon T, Wei S-E. et al. Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. arXiv e-prints 2016; arXiv:1611.08050
180 Cao Z, Hidalgo G, Simon T. et al. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. arXiv e-prints 2018; arXiv:1812.08008
181 Fang H-S, Li J, Tang H. et al. AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time. arXiv e-prints 2022; arXiv:2211.03375
182 Maji D, Nagori S, Mathew M. et al YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss. 2022 IEEE/CVF Conf Comput Vis Patt Recog Workshops (CVPRW) 2022; pp. 2636-2645
183 Sun K, Xiao B, Liu D. et al. Deep High-Resolution Representation Learning for Human Pose Estimation. arXiv e-prints 2019; arXiv:1902.09212
184 Zhou M, Stoffl L, Weygandt Mathis M. et al. Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity. . arXiv e-prints 2023; arXiv:2306.07879
185 Andriluka M, Iqbal U, Insafutdinov E. et al. PoseTrack: A Benchmark for Human Pose Estimation and Tracking. arXiv e-prints 2017; arXiv:1710.10000
186 Lin T-Y, Maire M, Belongie S. et al. Microsoft COCO: Common Objects in Context. In: Fleet D, Pajdla T, Schiele B et al. eds, Computer Vision – ECCV 2014. Cham: Springer International Publishing; 2014. pp. 740-755
187 Zhang W, Liu Z, Zhou L. et al. Martial Arts, Dancing and Sports dataset: A challenging stereo and multi-view dataset for 3D human pose estimation. Image Vis Comput 2017; 61: 22-39
188 Cioppa A, Giancola S, Deliege A. et al SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos. 2022 IEEE/CVF Conf Comput Vis Patt Recog Workshops (CVPRW) 2022; pp. 3490-3501
189 Xu Y, Zhang J, Zhang Q. et al. ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation. arXiv e-prints 2022; arXiv:2204.12484
190 Wang X, Tian Y, Geng F. et al. DFSTrack: Dual-stream fusion Siamese network for human pose tracking in videos. Image Vis Comput 2024; 148: 105117
191 NVIDIA. State-of-the-Art Real-time Multi-Object Trackers with NVIDIA DeepStream SDK 6.2 2024 https://developer.nvidia.com/blog/state-of-the-art-real-time-multi-object-trackers-with-nvidia-deepstream-sdk-6-2/ [accessed July 18, 2024]
192 Amrani E, Ben-Ari R, Shapira I. et al Self-Supervised Object Detection and Retrieval Using Unlabeled Videos. 2020 IEEE/CVF Conf Comput Vis Patt Recog Workshops (CVPRW) 2020; pp. 4100-4108
193 IBM. Meet watsonx 2024 https://www.ibm.com/watsonx?lnk=wmblp1 [accessed July 18, 2024]
194 Apple. Detecting human body poses in an image. 2024 https://developer.apple.com/documentation/coreml/detecting-human-body-poses-in-an-image [accessed July 18, 2024]
195 Chen YT, Yang JF, Tu KC. Smart Badminton Detection System Based on Scaled-YOLOv4. 2021 Int Symp Intell Signal Proc Commun Syst (ISPACS) 2021; pp. 1-2
196 Ghosh A, Singh S, Jawahar CV. Towards Structured Analysis of Broadcast Badminton Videos. 2018 IEEE Winter Conf Appl Comput Vis (WACV) 2018; pp. 296-304
197 Brumann C, Kukuk M, Reinsberger C. Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash. Sensors 2021; 21: 4550
198 Tahan O, Rady M, Sleiman N. et al. A computer vision driven squash players tracking system. 2018 19th IEEE Mediterranean Electrotechnical Conference (MELECON). 2018 pp. 155-159
199 Javadiha M, Andujar C, Lacasa E. et al. Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods. Sensors 2021; 21 (10) 3368
200 Fang C, Hsu C, Chiu C. et al Center Of Mass Trajectory: An Image Descriptor For Baseball Swing Analysis Based On Single Low-Cost Camera. 2021 IEEE Int Conf on Multimedia & Expo Workshops (ICMEW) 2021; pp. 1-5
201 Jiang Z, Ji H, Menaker S. et al GolfPose: Golf Swing Analyses with a Monocular Camera Based Human Pose Estimation. 2022 IEEE Int Conf on Multimedia and Expo Workshops (ICMEW) 2022; pp. 1-6
202 AlShami A, Boult T, Kalita J. Pose2Trajectory: Using transformers on body pose to predict tennis player’s trajectory. J Vis Commun Image Represent 2023; 97: 103954
203 Bao W, Niu T, Wang N. et al. Pose estimation and motion analysis of ski jumpers based on ECA-HRNet. Sci Rep 2023; 13: 6132
204 Dunnhofer M, Micheloni C. Visual tracking in camera-switching outdoor sport videos: Benchmark and baselines for skiing. Comput Vis Image Underst 2024; 243: 103978
205 Elfmark O, Ettema G, Groos D. et al. Performance Analysis in Ski Jumping with a Differential Global Navigation Satellite System and Video-Based Pose Estimation. Sensors 2021; 21: 5318
206 Ludwig K, Harzig P, Lienhart R. Detecting Arbitrary Intermediate Keypoints for Human Pose Estimation with Vision Transformers. 2022 IEEE/CVF Winter Conf Appl Comput Vis Workshops (WACVW) 2022; pp. 663-671
207 Zwölfer M, Heinrich D, Wandt B. et al. A graph-based approach can improve keypoint detection of complex poses: a proof-of-concept on injury occurrences in alpine ski racing. Sci Rep 2023; 13: 21465
208 De Bock J, Verstockt S. Video-Based Analysis and Reporting of Riding Behavior in Cyclocross Segments. Sensors 2021; 21: 7619
209 Host K, Ivasic-Kos M, Pobar M. Tracking Handball Players with the DeepSORT Algorithm. 9th International Conference on Pattern Recognition Applications and Methods. 2020
210 Host K, Pobar M, Ivasic-Kos M. Analysis of Movement and Activities of Handball Players Using Deep Neural Networks. J Imaging 2023; 9 (04) 80
211 Ivanovsky L, Matveev D, Khryashchev V. et al Detection and Tracking of Sport Players on Videodata Using Deep Learning Methods. 2022 International Siberian Conference on Control and Communications (SIBCON) 2022; pp. 1-5
212 Karungaru S, Matsuura K, Tanioka H. et al Ground Sports Strategy Formulation and Assistance Technology Development: Player Data Acquisition from Drone Videos. 2019 8th International Conference on Industrial Technology and Management (ICITM) 2019; pp. 322-325
213 Monezi LA, Calderani Junior A, Mercadante LA. et al. A Video-Based Framework for Automatic 3D Localization of Multiple Basketball Players: A Combinatorial Optimization Approach. Front Bioeng Biotechnol 2020; 8: 286
214 Wu KH, Tsai WL, Pan TY. et al Robust Basketball Player Tracking Based on a Hybrid Detection Grouping Framework for Overlapping Cameras. 2019 IEEE Int Conf Big Data (Big Data) 2019; pp. 5094-5100
215 Zhang R, Wu L, Yang Y. et al. Multi-camera multi-player tracking with deep player identification in sports video. Pattern Recognit 2020; 102: 107260
216 Kong L, Huang D, Wang Y. Long-Term Action Dependence-Based Hierarchical Deep Association for Multi-Athlete Tracking in Sports Videos. IEEE Transactions on Image Processing 2020; 29: 7957-7969
217 Tang G, Peng Z, Yin B. et al Tracking Players in Volleyball Matches using Vol-Bot-SORT. 2024 5th International Conference on Computer Engineering and Applications (ICCEA) 2024; pp. 1247-1250
218 Wang YP, Chu WT. Multiple Player Tracking With 3D Projection and Spatio-Temporal Information In Multi-View Sports Videos. ICASSP 2024 – 2024 IEEE Int Conf Acoustics Speech Signal Proc (ICASSP) 2024; pp. 9311-9315
219 Kalafatić Z, Hrkać T, Brkić K. Multiple Object Tracking for Football Game Analysis. 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO) 2022; pp. 936-941
220 Kim W, Moon S-W, Lee J. et al. Multiple player tracking in soccer videos: an adaptive multiscale sampling approach. Multimed Syst 2018; 24: 611-623
221 Naik BT, Hashmi MF, Geem ZW. et al. DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer. IEEE Access 2022; 10: 32494-32509
222 Wang T, Li T. Deep Learning-Based Football Player Detection in Videos. Comput Intell Neurosci 2022; 2022: 3540642
223 Yang Y, Zhang R, Wu W. et al Multi-camera Sports Players 3D Localization with Identification Reasoning. 2020 25th International Association for Pattern Recognition (ICPR) 2021; pp. 4497-4504
224 Lee J, Moon S, Nam DW. et al A Study on Sports Player Tracking based on Video using Deep Learning. 2020 International Conference on Information and Communication Technology Convergence (ICTC) 2020; pp. 1161-1163
225 Morais E, Ferreira A, Cunha SA. et al. A multiple camera methodology for automatic localization and tracking of futsal players. Pattern Recognit Lett 2014; 39: 21-30
226 Pádua PHCd, Pádua FLC, Sousa MTD. et al Particle Filter-Based Predictive Tracking of Futsal Players from a Single Stationary Camera. 2015 28th SIBGRAPI Conf Graphics Patterns Images 2015; pp. 134-141
227 Ludwig K, Scherer S, Einfalt M. et al Self-Supervised Learning for Human Pose Estimation in Sports. 2021 IEEE International Conference Multimedia & Expo Workshops (ICMEW) 2021; pp. 1-6
228 Zhang S, Lan S, Bu Q. et al YOLO based Intelligent Tracking System for Curling Sport. 2019 IEEE/ACIS 18th International Conference on Computer Science and Information Technology (ICIS) 2019; pp. 371-374
229 Huang W, Sun Y, Fu X. et al. A Novel LiDAR–Camera Fused Player Tracking System in Soccer Scenarios. IEEE Sensors J 2024; 24: 15630-15642
230 Scott A, Uchida I, Onishi M. et al SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2022; pp. 3568-3578
231 Winter DA. Biomechanics and motor control of human movement. 3rd edn. Hoboken, New Jersey: John Wiley & Sons; 2005
232 Fallahtafti F, Wurdeman SR, Yentes JM. Sampling rate influences the regularity analysis of temporal domain measures of walking more than spatial domain measures. Gait Posture 2021; 88: 216-220