DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

Video abnormality detection has become an essential component of surveillance video, identifying frames in the video sequences that contain events that do not conform to the expected behavior. However, their application is limited due to the presence of major challenges during training such as mode...

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Veröffentlicht in:	Cluster computing Jg. 27; H. 4; S. 4925 - 4940
Hauptverfasser:	Roka, Sanjay, Diwakar, Manoj
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2024 Springer Nature B.V
Schlagworte:	Ablation Algorithms Architecture Cameras Comparative analysis Computer Communication Networks Computer Science Deep learning Design Dictionaries Feature extraction Frames (data processing) Motion perception Motion stability Neural networks Operating Systems Processor Architectures Representations Spatiotemporal data Surveillance DSLSTM Video surveillance LSTM Deep convolutional encoder–decoder Crowd anomaly detection
ISSN:	1386-7857, 1573-7543
Online-Zugang:	Volltext
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