Search Results - Lucey, P.

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  1. 1

    Where will they go? Predicting fine-grained adversarial multi-agent motion using conditional variational autoencoders

    Felsen, P., Lucey, P., Ganguly, S.
    congress proceedings
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  2. 2

    Rugby-bot: Utilizing multi-task learning & fine-grained features for rugby league analysis

    Holbrook, M., Hobbs, J., Lucey, P.
    Published in arXiv e-print repository (2019)
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  3. 3

    Generating multi-agent trajectories using programmatic weak supervision

    Zhan, E., Zheng, S., Yue, Y., Sha, L., Lucey, P.
    Published in ICLR 2019 (2019)
    congress proceedings
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  4. 4

    You cannot do that Ben Stokes: Dynamically predicting shot type in cricket using a personalized deep neural network

    Gürpinar-Morgan, W., Dinsdale, D., Gallaghar, J., Cherukumudi, A., Lucey, P.
    congress proceedings
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  5. 5

    End-to-End Camera Calibration for Broadcast Videos

    Sha, L., Hobbs, J., Felsen, P., Wei, X., Lucey, P., Ganguly, S.
    congress proceedings
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  6. 6

    Fine-grained retrieval of sports plays using tree-based alignment of trajectories

    Sha, L., Lucey, P., Zheng, S., Kim, T., Yue, Y., Sridharan, S.
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  7. 7

    Interactive sports analytics: an intelligent interface for utilizing trajectories for interactive sports play retrieval and analytics

    Sha, L., Lucey, P., Yue, Y., Wei, X., Hobbs, J., Rohlf, C., Sridharan, S.
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  8. 8

    Recognising team activities from noisy data

    Bialkowski, A., Lucey, P., Carr, P., Denman, S., Matthews, I., Sridharan, S.
    congress proceedings
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  9. 9

    Quantifying the value of transitions in soccer via spatiotemporal trajectory clustering

    Hobbs, J., Power, P., Sha, L., Ruiz, H., Lucey, P.
    Published 2018
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  10. 10

    Mythbusting set-pieces in soccer

    Power, P., Hobbs, J., Ruiz, H., Wei, X., Lucey, P.
    Published 2018
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  11. 11

    Bhostgusters: Realtime interactive play sketching with synthesized NBA defenses

    Seidl, T., Cherukumudi, A., Hartnett, A., Carr, P., Lucey, P.
    Published 2018
    congress proceedings
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  12. 12

    Understanding and analyzing a large collection of archived swimming videos

    Sha, L., Lucey, P., Morgan, S., Sridharan, S., Pease, D.
    congress proceedings
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  13. 13

    Assessing team strategy using spatiotemporal data

    Lucey, P., Oliver, D., Carr, P., Roth, J., Matthews, I.
    congress proceedings
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  14. 14

    Representing and discovering adversarial team behaviors using player roles

    Lucey, P., Bialkowski, A., Carr, P., Morgan, S., Matthews, I., Sheikh, Y.
    congress proceedings
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  15. 15

    Large-scale analysis of formations in soccer

    Wei, X., Sha, L., Lucey, P., Morgan, S., Sridharan, S.
    congress proceedings
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  16. 16

    Predicting shot locations in tennis using spatiotemporal data

    Wei, X., Lucey, P., Morgan, S., Sridharan, S.
    congress proceedings
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  17. 17

    Swimmer localization from a moving camera

    Sha, L., Lucey, P., Sridharan, S., Morgan, S., Peas, D.
    congress proceedings
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  18. 18

    Sweet-spot: Using spatiotemporal data to discover and predict shots in tennis

    Wei, X., Lucey, P., Morgan, S., Sridharan, S.
    congress proceedings
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  19. 19

    Characterizing multi-agent team behavior from partial team tracings: Evidence from the English Premier League

    Lucey, P., Bialkowski, A., Carr, P., Matthews, I., Sheikh, Y.
    congress proceedings
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  20. 20

    Forecasting the next shot location in tennis using fine-grained spatiotemporal tracking data

    Wei, X., Lucey, P., Morgan, S., Sridharan, S.
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