Understanding the Limits of 2D Skeletons for Action Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00118833" target="_blank" >RIV/00216224:14330/21:00118833 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00530-021-00754-0" target="_blank" >https://link.springer.com/article/10.1007/s00530-021-00754-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00530-021-00754-0" target="_blank" >10.1007/s00530-021-00754-0</a>
Alternative languages
Result language
angličtina
Original language name
Understanding the Limits of 2D Skeletons for Action Recognition
Original language description
With the development of motion capture technologies, 3D action recognition has become a popular task that finds great applicability in many areas, such as augmented reality, human–computer interaction, sports, or healthcare. On the other hand, the acquisition of 3D human skeleton data is an expensive and time-consuming process, mainly due to the high costs of capturing technologies and the absence of suitable actors. We overcome these issues by focusing on the 2D skeleton modality that can be easily extracted from ordinary videos. The objective of this work is to demonstrate a high descriptive power of such a 2D skeleton modality by achieving accuracy on the task of daily action recognition competitive to 3D skeleton data. More importantly, we thoroughly analyze the factors that significantly influence the 2D recognition accuracy, such as the sensitivity towards data normalization, scaling, quantization, and 3D-to-2D distortions in skeleton orientations and sizes, which are caused by the loss of depth dimension and fixed-angle camera view. We also provide valuable insights on how to mitigate these problems to increase recognition accuracy significantly. The experimental evaluation is conducted on three datasets different in nature. The ability to learn different types of actions better using either 2D or 3D skeletons is also reported. Throughout experiments, a generic light-weight LSTM network is used, whose architecture can be easily tuned to achieve the desired trade-off between its accuracy and efficiency. We show that the proposed approach achieves not only the state-of-the-art results in 2D skeleton action recognition but is also highly competitive to the best-performing methods classifying 3D skeleton sequences or the visual content extracted from ordinary videos.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Multimedia Systems
ISSN
0942-4962
e-ISSN
1432-1882
Volume of the periodical
27
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
547-561
UT code for WoS article
000615767700001
EID of the result in the Scopus database
2-s2.0-85100576467