Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods. This paper introduces an unsupervised end-to-end architecture for video anomaly detection that applies...

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Veröffentlicht in:	IEEE access Jg. 12; S. 104770 - 104784
Hauptverfasser:	Niaz, Asim, Ul Amin, Sareer, Soomro, Shafiullah, Zia, Hamza, Nam Choi, Kwang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection autoencoders Computer Science computer vision Crime prevention Datavetenskap Effectiveness Encoders-Decoders Feature extraction intelligent surveillance systems Optical flow Pedestrians Predictive models Public safety Representations Surveillance three-dimensional convolutional neural network (3DCNN) video-based abnormal event detection Videos
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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