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ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14361))

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Abstract

We present ArcheryVis, a tool for analyzing and visualizing archery performance data collected from two elementary school trainees over a year. The goals are to digitally archive their training target papers, automatically detect and calibrate shots, and analyze their performance via a visual interface. We achieve automatic shot detection using a deep neural network, compute scores and relevant statistical measures, and design coordinated multiple views for interactive user exploration. Experimental results demonstrate the effectiveness of ArcheryVis.

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Acknowledgements

This research was supported in part by the U.S. National Science Foundation through grants DUE-1833129, IIS-1955395, IIS-2101696, and OAC-2104158. The authors thank Tram Trinh and Janet Meng, who contributed to the project’s development, and the anonymous reviewers for their comments.

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Authors and Affiliations

  1. University of Notre Dame, Notre Dame, IN, 46556, USA

    Zhiyuan Cheng, Zeyuan Li, Mayleen Liu, Jonathan D’Alonzo & Chaoli Wang

  2. Columbia University, New York, NY, 10027, USA

    Zhepeng Luo

Authors
  1. Zhiyuan Cheng
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  2. Zeyuan Li
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  3. Zhepeng Luo
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  4. Mayleen Liu
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  5. Jonathan D’Alonzo
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  6. Chaoli Wang
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Corresponding author

Correspondence to Chaoli Wang .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Cheng, Z., Li, Z., Luo, Z., Liu, M., D’Alonzo, J., Wang, C. (2023). ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-47969-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47968-7

  • Online ISBN: 978-3-031-47969-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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