Content-based Management of Human Motion Data: Survey and Challenges

Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent y...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.1-1
Main Authors: Sedmidubsky, Jan, Elias, Petr, Budikova, Petra, Zezula, Pavel
Format: Article
Language:English
Subjects:
Action detection
content-based processing
Data management
Data processing
deep features
Human motion
Immune system
metric learning
motion capture data
Motion segmentation
Representations
Segmentation
Semantics
Similarity
Skeleton
skeleton sequences
sub-sequence search
Task analysis
Three-dimensional displays
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Summary:Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3075766