Search Results - Image and Vision Computing

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  1. 1

    Foul prediction with estimated poses from soccer broadcast video

    Fang, J., Yeung, C., Fujii, K.
    Published in Sports Engineering (2025)
    “…Recent advances in computer vision have led to significant progress in tracking and pose estimation of sports players. …”
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  2. 2

    Jump-Aware: player position rectification and identification in dynamic sports using jump event spotting

    Oo, Y. M., Jamsrandorj, A., Chao, V., Nguyen, H. Q., Hwang, Y., Mun, K.-R., Kim, J.
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  3. 3

    From beats to scores: a multi-modal framework for comprehensive figure skating assessment

    Wang, F., Wang, Q., Chen, D.
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  4. 4

    AthletePose3D: a benchmark dataset for 3D human pose estimation and kinematic validation in athletic movements

    Yeung, C., Suzuki, T., Tanaka, R., Yin, Z., Fujii, K.
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  5. 5

    TT3D: table tennis 3D reconstruction

    Gossard, T., Ziegler, A., Zell, A.
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  6. 6

    Finish-line photography system based on multi-scale convolutional neural network deblurring

    Jin, Z., Tian, W. “…In sports science research, the dynamic non-uniform blur caused by the movement of running athletes is a challenging problem in computer vision, that seriously affects the judgment accuracy of the finish-line photography system. …”
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  7. 7

    Rugby scene classification enhanced by vision language model

    Nonaka, N., Fujihira, R., Koshiba, T., Maeda, A., Seita, J.
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  8. 8

    Analysis of movement and activities of handball players using deep neural networks

    Host, K., Pobar, M., Ivasic-Kos, M.
    Published in Journal of Imaging (2023)
    “…The aim of the paper is to explore the computer vision-based solutions for recognizing player actions that can be applied in unconstrained handball scenes with no additional sensors and with modest requirements, allowing a broader adoption of computer vision applications in both professional and amateur settings. …”
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  9. 9

    Temporal pattern attention for multivariate time series of tennis strokes classification

    Skublewska-Paszkowska, M., Powroznik, P.
    Published in Sensors (2023)
    “…It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. …”
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  10. 10

    SwimmerNET: underwater 2D swimmer pose estimation exploiting fully convolutional neural networks

    Giulietti, N., Caputo, A., Chiariotti, P., Castellini, P.
    Published in Sensors (2023)
    “…In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. …”
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  11. 11

    Estimating ground reaction forces from two-dimensional pose data: a biomechanics-based comparison of AlphaPose, BlazePose, and OpenPose

    Mundt, M., Born, Z., Goldacre, M., Alderson, J.
    Published in Sensors (2023)
    “…The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. …”
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  12. 12

    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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  13. 13

    Investigation on combining 3D convolution of image data and optical flow to generate temporal action proposals

    Schlosser, P., Münch, D., Arens, M.
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  14. 14

    Classification of puck possession events in ice hockey

    Tora, M. R., Chen, J., Little, J. J.
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  15. 15

    Accurate and efficient 3D human pose estimation algorithm using single depth images for pose analysis in golf

    Park, S., Chang, J. Y., Jeong, H., Lee, J.-H., Park, J.-Y.
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  16. 16

    Athlete pose estimation by a global-local network

    Hwang, J., Park, S., Kwak, N.
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  17. 17

    Application of computer vision and vector space model for tactical movement classification in badminton

    Weeratunga, K., Dharmaratne, A., How, K. B. “…The proposed approach uses computer vision techniques to automate data gathering from video footage. …”
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  18. 18

    Automatic tactical adjustment in real-time: modeling adversary formations with radon-cumulative distribution transform and canonical correlation analysis

    Rahimi, A. M., Kolouri, S., Bhattacharyya, R.
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  19. 19

    Football action recognition using hierarchical LSTM

    Tsunoda, T., Komori, Y., Matsugu, M., Harada, T.
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  20. 20

    3D ball localization from a single calibrated image

    Van Zandycke, G., De Vleeschouwer, C.
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