demonstration

A Multi-Modal 3D Capturing Platform for Learning and Preservation of Traditional Sports and Games

Authors:

Anargyros Chatzitofis,

Nadia Magnenat ThalmannAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 747 - 748

https://doi.org/10.1145/2733373.2807975

Published: 13 October 2015 Publication History

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Abstract

We present a demonstration of a multi-modal 3D capturing platform coupled to a motion comparison system. This work is focused on the preservation of Traditional Sports and Games, namely the Gaelic sports from Ireland and Basque sports from France and Spain. Users can learn, compare and compete in the performance of sporting gestures and compare themselves to real athletes. Our online gesture database provides a way to preserve and display a wide range of sporting gestures. The capturing devices utilised are Kinect 2 sensors and wearable inertial sensors, where the number required varies based on the requested scenario. The fusion of these two capture modalities, coupled to our inverse kinematic algorithm, allow us to synthesize a fluid and reliable 3D model of the user gestures over time. Our novel comparison algorithms provide the user with a performance score and a set of comparison curves (i.e. joint angles and angular velocities), providing a precise and valuable feedback for coaches and players.

References

[1]

F. Destelle, A. Ahmadi, N. E. O'Connor et al. Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In EUSIPCO '14, pages 371--375, Sept 201

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[2]

L. Unzueta, J. Goenetxea, M. Rodriguez, and M. Linaza. Viewpoint-dependent 3d human body posing for sports legacy recovery from images and video. In EUSIPCO '14, pages 361--365, Sept 2014.

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Cited By

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Li HFeng BSu MLiu Z(2024)Design of Interactive Digital Virtual Display Application for Chinese Heritage Traditional Herbal Medicine CultureCulture and Computing10.1007/978-3-031-61147-6_19(274-292)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-61147-6_19
Chen JMeng JWang XYuan J(2020)Dynamic Graph CNN for Event-Camera Based Gesture Recognition2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9181247(1-5)Online publication date: Oct-2020
https://doi.org/10.1109/ISCAS45731.2020.9181247
Wang QZhang YYuan JLu Y(2019)Space-Time Event Clouds for Gesture Recognition: From RGB Cameras to Event Cameras2019 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2019.00199(1826-1835)Online publication date: Jan-2019
https://doi.org/10.1109/WACV.2019.00199
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Index Terms

A Multi-Modal 3D Capturing Platform for Learning and Preservation of Traditional Sports and Games
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Computer graphics
    1. Image manipulation

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Information

Published In

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

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

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Published: 13 October 2015

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Dublin City University

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SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

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MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Li HFeng BSu MLiu Z(2024)Design of Interactive Digital Virtual Display Application for Chinese Heritage Traditional Herbal Medicine CultureCulture and Computing10.1007/978-3-031-61147-6_19(274-292)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-61147-6_19
Chen JMeng JWang XYuan J(2020)Dynamic Graph CNN for Event-Camera Based Gesture Recognition2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9181247(1-5)Online publication date: Oct-2020
https://doi.org/10.1109/ISCAS45731.2020.9181247
Wang QZhang YYuan JLu Y(2019)Space-Time Event Clouds for Gesture Recognition: From RGB Cameras to Event Cameras2019 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2019.00199(1826-1835)Online publication date: Jan-2019
https://doi.org/10.1109/WACV.2019.00199
Monaghan DHonohan FAhmadi AMcDaniel TTadayon RKarpur AMorran KO'Connor NPanchanathan SHanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)A Multimodal Gamified Platform for Real-Time User Feedback in Sports PerformanceProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2973815(708-710)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1145/2964284.2973815
Ahmadi ADestelle FUnzueta LMonaghan DLinaza MMoran KO'Connor N(2016)3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic ModelingIEEE Sensors Journal10.1109/JSEN.2016.259301116:24(8823-8831)Online publication date: 15-Dec-2016
https://doi.org/10.1109/JSEN.2016.2593011

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Capturing and visualising playground games and performance: a Wii and Kinect based motion capture system

Optimized Motion Capture System for Full Body Human Motion Capturing Case Study of Educational Institution and Small Animation Production

Capturing and animating skin deformation in human motion

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