Hierarchical Two-Stream Growing Self-Organizing Maps With Transience for Human Activity Recognition

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art vi...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 16; H. 12; S. 7756 - 7764
Hauptverfasser:	Nawaratne, Rashmika, Alahakoon, Damminda, De Silva, Daswin, Kumara, Harsha, Yu, Xinghuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Activity recognition Algorithms Architecture Decision making Deep learning Feature extraction Forgetting hierarchical learning Histograms Human activity recognition human activity recognition (HAR) Machine learning Moving object recognition neural networks Neurons Optical flow Self organizing maps Self-organizing feature maps Surveillance Video data Video surveillance
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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