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  1. 1

    Application of deep learning methods for detection and tracking of players

    Ivasic-Kos, M., Host, K., Pobar, M.
    Published 2022
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  2. 2

    Neuer Service der RTK: DHB-TrainercenterTV - Das digitale Handwerkszeug für alle Trainer (New service of the RTK: DHB-TrainercenterTV - The digital tool for all trainers )

    Beppler, J.
    Published in Handballtraining (2021)
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  3. 3

    Quantifying throwing load in handball: a method for measuring the number of throws

    Skejø, S. D., Liaghat, B., Jakobsen, C. C., Møller, M., Bencke, J., Papi, G., Kunwald, N. P., Sorensen, H.
    Published in Sports Biomechanics (2021)
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  4. 4

    Innovacionnaja tehnologija komp'juternogo testirovanija psihomotoriki v sportivnyh igrah (Innovative technology in computer testing of psycho-motor in team sports)

    Lisenchuk, G., Tyshchenko, V.
    Published in Science in Olympic Sport (2019)
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  5. 5

    Wer nicht fragt, der nicht gewinnt. Vereins- und mannschaftsinterne Anpassungen auf der Basis einer Saisonauswertung per individuellem Online-Fragebogen (If you don't ask, you don't win. Club- and team-internal adjustments on the basis of a season evaluation with individual online questionnaire)

    Grgic, M.
    Published in Handballtraining (2021)
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  6. 6

    Performance prediction and evaluation in female handball players using machine learning models

    Oytun, M., Tinazci, C., Sekeroglu, B., Acikada, C., Yavuz, H. U.
    Published in IEEE Access (2020)
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  7. 7

    A graded concept of an information model for evaluating performance in team handball

    Schwenkreis, F.
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  8. 8

    Active player detection in handball videos using optical flow and STIPs based measures

    Ivasic-Kos, M., Pobar, M., Gonzàlez, J.
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  9. 9

    Mask R-CNN and optical flow based method for detection and marking of handball actions

    Pobar, M., Ivasic-Kos, M.
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  10. 10

    Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS

    Ivasic-Kos, M., Pobar, M.
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  11. 11

    Detection of the leading player in handball scenes using Mask R-CNN and STIPS

    Pobar, M., Ivasic-Kos, M.
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  12. 12

    Model-based behavioral causality analysis of handball with delayed transfer entropy

    Itoda, K., Watanabe, N., Takefuji, Y.
    Published in Procedia - Computer Science (2015)
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  13. 13

    System for monitoring and advanced analysis of handball matches

    Hlupic, T., Jandrijevic, F., Kovacev, J., Petricioli, L., Gracin, T., Baranovic, M.
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  14. 14

    360° virtual reality: A SWOT analysis in comparison to virtual reality

    Kittel, A., Larkin, P., Cunningham, I., Spittle, M.
    Published in Frontiers in Psychology (2020)
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  15. 15

    Learning-based tracking of fast moving objects

    Zita, A., Sroubek, F.
    Published in arXiv e-print repository (2020)
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  16. 16

    Tracking handball players with the DeepSORT algorithm

    Host, K., Ivasic-Kos, M., Pobar, M.
    Published 2020
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  17. 17

    Active player detection in handball scenes based on activity measures

    Pobar, M., Ivasic-Kos, M.
    Published in Sensors (2020)
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  18. 18

    Object detection using synthesized data

    Buric, M., Paulin, G., Ivašic-Kos, M.
    Published in ICT Innovations 2019 (2019)
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  19. 19

    Using data analytics to make the scouting and training of sports talents more effective

    Blom, C.
    Published 2019
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  20. 20

    Leistungsbewertung per PlayerScore. Ein Verfahren zur zahlenbasierten Einschätzung der Wettkampfleistung von Handballern (Performance evaluation with PlayerScore. An evaluation procedure for handball players based on figures)

    Uhrmeister, J., Brosig, O.
    Published in Handballtraining (2019)
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