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demonstration

A do-it-yourself computer vision based robotic ball throw trainer

Published: 24 June 2021 Publication History

Abstract

We demonstrate a self-training system for sports involving throwing a ball. We design a do-it-yourself (DIY) machinery that can be assembled using off-the-shelf items and integrates computer vision to visually track the ball throw accuracy. In this work, we demonstrate a system that can identify if the ball went through the hoop and approximately in which of the hoop's inner region. We envision that this preliminary design sets the foundation for a complete DIY sports IoT system that involves a hoola hoop, RaspberryPi, PiCamera and a LED strip, along with advanced ball placement and dynamics tracking.

References

[1]
University of Georgia | College Athletics Financial Information (CAFI) Database. http://cafidatabase.knightcommission.org/fbs/sec/university-of-georgia, May 2021.
[2]
Georgia Southern University | College Athletics Financial Information (CAFI) Database. http://cafidatabase.knightcommission.org/fbs/sunbelt/georgia-southern-university, May 2021.
[3]
National Collegiate Athletic Association (NCAA) Research. 15-Year Trends in Division III Athletics Finances. https://ncaaorg.s3.amazonaws.com/research/Finances/2020RES_D3-RevExp_Report.pdf, 2020.
[4]
Front Office Sports RSS Feed. Amid Pandemic, NFL and College Stars Turn to Robot QB For Reps. https://frontofficesports.com/the-seeker-robot-qb/, December 2020.

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cover image ACM Conferences
MobiSys '21: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services
June 2021
528 pages
ISBN:9781450384438
DOI:10.1145/3458864
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2021

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  • Demonstration

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MobiSys '21
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MobiSys '21 Paper Acceptance Rate 36 of 166 submissions, 22%;
Overall Acceptance Rate 274 of 1,679 submissions, 16%

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