Cited By
View all- Budikova PKlepac DRusnak DSlovak M(2022)Visual Exploration of Human Motion DataSimilarity Search and Applications10.1007/978-3-031-17849-8_6(64-71)Online publication date: 5-Oct-2022
Recognizing User-Defined Subsequences in Human Motion Data
Pages 395 - 398
Abstract
Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes.
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Index Terms
- Recognizing User-Defined Subsequences in Human Motion Data
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Information & Contributors
Information
Published In
June 2019
427 pages
ISBN:9781450367653
DOI:10.1145/3323873
- General Chairs:
- Abdulmotaleb El Saddik,
- Alberto Del Bimbo,
- Zhongfei Zhang,
- Program Chairs:
- Alexander Hauptmann,
- K. Selcuk Candan,
- Marco Bertini,
- Lexing Xie,
- Xiao-Yong Wei
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Published: 05 June 2019
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View all- Budikova PKlepac DRusnak DSlovak M(2022)Visual Exploration of Human Motion DataSimilarity Search and Applications10.1007/978-3-031-17849-8_6(64-71)Online publication date: 5-Oct-2022
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