Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from...

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Auteurs principaux: Faisal Mehmood, Enqing Chen, Muhammad Azeem Akbar, Abeer Abdulaziz Alsanad
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
human action recognition (HAR)
skeleton data
spatiotemporal
multi-task learning network (MTLN)
convolutional neural network (CNN)
Electronics
TK7800-8360
Accès en ligne:https://doaj.org/article/ef9865d76f654905b711d50b6b7c2dd9
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