| Field |
Value |
Language |
|
dc.contributor.author |
Kulbacki, M |
|
|
dc.contributor.author |
Segen, J |
|
|
dc.contributor.author |
Chaczko, Z
https://orcid.org/0000-0002-2816-7510
|
|
|
dc.contributor.author |
Rozenblit, JW |
|
|
dc.contributor.author |
Kulbacki, M |
|
|
dc.contributor.author |
Klempous, R |
|
|
dc.contributor.author |
Wojciechowski, K |
|
|
dc.date.accessioned |
2024-04-19T07:13:47Z |
|
|
dc.date.available |
2023-04-21 |
|
|
dc.date.available |
2024-04-19T07:13:47Z |
|
|
dc.date.issued |
2023-04-25 |
|
|
dc.identifier.citation |
Sensors (Basel), 2023, 23, (9), pp. 4258 |
|
|
dc.identifier.issn |
1424-8220 |
|
|
dc.identifier.issn |
1424-8220 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/178091
|
|
|
dc.description.abstract |
The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments. |
|
|
dc.format |
Electronic |
|
|
dc.language |
eng |
|
|
dc.publisher |
MDPI |
|
|
dc.relation.ispartof |
Sensors (Basel) |
|
|
dc.relation.isbasedon |
10.3390/s23094258 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering |
|
|
dc.subject.classification |
Analytical Chemistry |
|
|
dc.subject.classification |
3103 Ecology |
|
|
dc.subject.classification |
4008 Electrical engineering |
|
|
dc.subject.classification |
4009 Electronics, sensors and digital hardware |
|
|
dc.subject.classification |
4104 Environmental management |
|
|
dc.subject.classification |
4606 Distributed computing and systems software |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Pattern Recognition, Automated |
|
|
dc.subject.mesh |
Human Activities |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Human Activities |
|
|
dc.subject.mesh |
Pattern Recognition, Automated |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Pattern Recognition, Automated |
|
|
dc.subject.mesh |
Human Activities |
|
|
dc.title |
Intelligent Video Analytics for Human Action Recognition: The State of Knowledge. |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
23 |
|
|
utslib.location.activity |
Switzerland |
|
|
utslib.for |
0301 Analytical Chemistry |
|
|
utslib.for |
0502 Environmental Science and Management |
|
|
utslib.for |
0602 Ecology |
|
|
utslib.for |
0805 Distributed Computing |
|
|
utslib.for |
0906 Electrical and Electronic Engineering |
|
|
pubs.organisational-group |
University of Technology Sydney |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications |
|
|
pubs.organisational-group |
University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2024-04-19T07:13:41Z |
|
|
pubs.issue |
9 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
23 |
|
|
utslib.citation.issue |
9 |
|
