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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Vydané v:	IEEE access Ročník 12; s. 104770 - 104784
Hlavní autori:	Niaz, Asim, Ul Amin, Sareer, Soomro, Shafiullah, Zia, Hamza, Nam Choi, Kwang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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