research-article

Similarity Search in 3D Human Motion Data

Authors:

Jan Sedmidubsky,

Pavel ZezulaAuthors Info & Claims

ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

Pages 5 - 6

https://doi.org/10.1145/3323873.3326589

Published: 05 June 2019 Publication History

Abstract

Motion capture technologies can digitize human movements into a discrete sequence of 3D skeletons. Such spatio-temporal data have a great application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. The objective of this tutorial is to explain fundamental principles and technologies designed for searching, subsequence matching, classification and action detection in the 3D human motion data. These operations inherently require the concept of similarity to determine the degree of accordance between pairs of 3D skeleton sequences. Such similarity can be modeled using a generic approach of metric space by extracting effective deep features and comparing them by efficient distance functions. The metric-space approach also enables applying traditional index structures to efficiently access large datasets of skeleton sequences. We demonstrate the functionality of selected motion-processing operations by interactive web applications.

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Jan Sedmidubsky, Petr Elias, and Pavel Zezula. 2019. Searching for variable-speed motions in long sequences of motion capture data. Information Systems, Vol. 80 (2019), 148--158.

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Digital Library

Cited By

Rehan MWannous HAlkheir JAboukassem K(2022)Learning Co-occurrence Features Across Spatial and Temporal Domains for Hand Gesture RecognitionProceedings of the 19th International Conference on Content-based Multimedia Indexing10.1145/3549555.3549591(36-42)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3549555.3549591
Ma HLi JHosseini RTomizuka MChoi C(2022)Multi-Objective Diverse Human Motion Prediction with Knowledge Distillation2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00799(8151-8161)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00799
Li BYuan JYe YLu YZhang CTian Q(2021)3D sketching for 3D object retrievalMultimedia Tools and Applications10.1007/s11042-020-10033-180:6(9569-9595)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11042-020-10033-1
Show More Cited By

Index Terms

Similarity Search in 3D Human Motion Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

June 2019

427 pages

ISBN:9781450367653

DOI:10.1145/3323873

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Alberto Del Bimbo
University of Florence, Italy
,
Zhongfei Zhang
Binghamton University, State University of New York, USA
,
Program Chairs:
Alexander Hauptmann
Carnegie Mellon University, USA
,
K. Selcuk Candan
Arizona State University, USA
,
Marco Bertini
University of Florence, Italy
,
Lexing Xie
Australia National University, Australia
,
Xiao-Yong Wei
Sichuan University, China

Copyright © 2019 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2019

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Grantová Agentura ðeské Republiky

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ICMR '19

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SIGMM

ICMR '19: International Conference on Multimedia Retrieval

June 10 - 13, 2019

Ottawa ON, Canada

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Rehan MWannous HAlkheir JAboukassem K(2022)Learning Co-occurrence Features Across Spatial and Temporal Domains for Hand Gesture RecognitionProceedings of the 19th International Conference on Content-based Multimedia Indexing10.1145/3549555.3549591(36-42)Online publication date: 14-Sep-2022
https://dl.acm.org/doi/10.1145/3549555.3549591
Ma HLi JHosseini RTomizuka MChoi C(2022)Multi-Objective Diverse Human Motion Prediction with Knowledge Distillation2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00799(8151-8161)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00799
Li BYuan JYe YLu YZhang CTian Q(2021)3D sketching for 3D object retrievalMultimedia Tools and Applications10.1007/s11042-020-10033-180:6(9569-9595)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11042-020-10033-1
Yahya-Imam MAlao M(2021)An Empirical Study on Selected Emerging Technologies: Strengths and ChallengesIntelligent Computing and Innovation on Data Science10.1007/978-981-16-3153-5_15(115-125)Online publication date: 28-Sep-2021
https://doi.org/10.1007/978-981-16-3153-5_15

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