Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomal...

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Veröffentlicht in:	SN computer science Jg. 5; H. 1; S. 190
Hauptverfasser:	Almahadin, Ghayth, Subburaj, Maheswari, Hiari, Mohammad, Sathasivam Singaram, Saranya, Kolla, Bhanu Prakash, Dadheech, Pankaj, Vibhute, Amol D., Sengan, Sudhakar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 11.01.2024 Springer Nature B.V
Schlagworte:	Algorithms Anomalies Automation Behavior Cameras Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Datasets Deep learning Frames (data processing) Information Systems and Communication Service Localization Original Research Pattern recognition Pattern Recognition and Graphics Pedestrians Soft Computing in Engineering Applications Software Engineering/Programming and Operating Systems Spatial data Supervised learning Surveillance Video data Vision Walkways Suspicious activities LSTM Spatio-temporal features Autoencoders Anomaly detection Machine learning
ISSN:	2661-8907, 2662-995X, 2661-8907
Online-Zugang:	Volltext
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