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  1. 1

    Automated video assistant referee in lead climbing

    Künzler, E., Roder, D., Stöcker, U., Wolf, P.
    Published in Current Issues in Sport Science (2025)
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  2. 2

    autoWT: a semi-automated ML-based movement tracking system for performance tracking and analysis in Olympic weightlifting

    Jurkans, G., Ekárt, A., Bernardet, U.
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  3. 3

    The development of automatic training analysis using 3D accelerometer in male young elite soccer team

    Magome, T., Tanaka, T., Yamaguchi, T.
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  4. 4

    A system for automatic and fast gymnastics pose estimation and key frame identification

    Masmoudi, I., Link, J., Möck, S., Nissinen, P., Koelewijn, A.
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  5. 5

    Detection of shot information using footwork trajectory and skeletal information of badminton players

    Tanaka, N., Shishido, H., Suita, M., Nishijima, T., Kameda, Y., Kitahara, I.
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  6. 6

    2trax3: raising accessibility and everyday use of automatic motion analysis in (combat) sports via ml enhanced 2D to 3D estimation algorithms

    Duvelek, S., Hoelbling, D., Baranyi, R., Breiteneder, R., Pinter, K., Grechenig, T.
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  7. 7

    Analyzing micro parameters of diagonal stride in cross-country skiing using microsensors on snow

    Rindal, O. M. H., Gløersen, Ø., Tjønnås, J.
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  8. 8

    Individual in-situ GPS-derived acceleration-speed profiling: Toward automatization and refinement in male professional rugby union players

    Miguens, N., Brocherie, F., Moulié, L., Milhet, P., Bon, M., Lassus, P., Toussaint, J.-F., Sedeaud, A.
    Published in Sports Medicine - Open (2024)
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  9. 9

    Automatic detection of paraswimmers front crawl key points to assess upper-limb coordination

    Regaieg, M.
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  10. 10

    Automated service height fault detection using computer vision and machine learning for badminton matches

    Goh, G. L., Goh, G. D., Pan, J. W., Teng, P. S. P., Kong, P. W.
    Published in Sensors (2023)
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  11. 11

    Ingenuity of analysis using AI for platform diving

    Nomura, T., Kida, N.
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  12. 12

    Measuring instantaneous velocity in four swim strokes using an automatic head tracking system: a comparison study

    Scott, M., Elsworthy, N., Elipot, M., Brackley, V., Kean, C. O.
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  13. 13

    Pose estimation or manual digitising: can automating technologies change the current in-field assessment of high jump?

    Goldacre, M.
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  14. 14

    Movement onset detection methods: a comparison using force plate recordings

    Pinto, B. L., Callaghan, J. P.
    Published in Journal of Applied Biomechanics (2023)
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  15. 15

    Sensor-based jump detection and classification with machine learning in trampoline gymnastics

    Woltmann, L., Hartmann, C., Lehner, W., Rausch, P., Ferger, K.
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  16. 16

    An automated method to extract three-dimensional position data using an infrared time-of-flight camera

    Dunn, M., Pavan, D., Ramirez, P., Rava, S., Sharin, A.
    Published in Proceedings (2018)
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  17. 17

    Part-based player identification using deep convolutional representation and multi-scale pooling

    Senocak, A., Oh, T.-H., Kim, J., Kweon, I. S.
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  18. 18

    A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games

    Cioppa, A., Deliège, A., Van Droogenbroeck, M.
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  19. 19

    Associative embedding for team discrimination

    Istasse, M., Moreau, J., De Vleeschouwer, C.
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  20. 20

    Pose-guided R-CNN for jersey number recognition in sports

    Liu, H., Bhanu, B.
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