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Child Action Recognition in RGB and RGB-D Data

Published: 01 April 2020 Publication History

Abstract

The paper presents an ongoing work that aims for real-time action recognition specifically tailored for child-centered research. To this end, we collected and annotated a dataset of 200 primary school children aged 6 to 11 years old. Each child was asked to perform seven actions: boxing, waving, clapping, running, jogging, walking towards the camera, and walking from side to side. Two camera perspectives are provided, with a top view in RGB format and a frontal view in both RGB and RGB-D formats. Body keypoints (skeleton data) are extracted using OpenPose and OpenNI tools. The results of this work are expected to bridge the performance gap between activity recognition systems for adults and children.

References

[1]
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2018. OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. In arXiv preprint arXiv:1812.08008 .
[2]
James Kennedy, Séverin Lemaignan, Caroline Montassier, Pauline Lavalade, Bahar Irfan, Fotios Papadopoulos, Emmanuel Senft, and Tony Belpaeme. 2017. Child speech recognition in human-robot interaction: evaluations and recommendations. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 82--90.
[3]
Anara Sandygulova, Yerdaulet Absattar, Damir Doszhan, and German I Parisi. 2016. Child-centred motion-based age and gender estimation with neural network learning. In Workshops at the Thirtieth AAAI Conference on Artificial Intelligence .
[4]
Anara Sandygulova, Mauro Dragone, and Gregory MP O'Hare. 2014. Real-time adaptive child-robot interaction: Age and gender determination of children based on 3d body metrics. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 826--831.
[5]
Anara Sandygulova, Wafa Johal, Zhanel Zhexenova, Bolat Tleubayev, Aida Zhanatkyzy, Aizada Turarova, Zhansaule Telisheva, Anna CohenMiller, Thibault Asselborn, and Pierre Dillenbourg. 2020. CoWriting Kazakh: Learning a New Script with a Robot. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20). Cambridge, United Kingdom. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3319502.3374813
[6]
Anara Sandygulova and Gregory MP O'Hare. 2018. Age-and gender-based differences in children's interactions with a gender-matching robot. International Journal of Social Robotics, Vol. 10, 5 (2018), 687--700.
[7]
Christian Schuldt, Ivan Laptev, and Barbara Caputo. 2004. Recognizing human actions: a local SVM approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., Vol. 3. IEEE, 32--36.

Cited By

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  • (2023)Multiview child motor development dataset for AI-driven assessment of child developmentGigaScience10.1093/gigascience/giad03912Online publication date: 27-May-2023

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cover image ACM Conferences
HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
March 2020
702 pages
ISBN:9781450370578
DOI:10.1145/3371382
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: 01 April 2020

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Author Tags

  1. action recognition
  2. children
  3. openpose
  4. pose estimation

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Overall Acceptance Rate 192 of 519 submissions, 37%

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  • (2023)Multiview child motor development dataset for AI-driven assessment of child developmentGigaScience10.1093/gigascience/giad03912Online publication date: 27-May-2023

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