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

• Published in: SN computer science Vol. 5; no. 1; p. 190
• Main Authors: Almahadin, Ghayth, Subburaj, Maheswari, Hiari, Mohammad, Sathasivam Singaram, Saranya, Kolla, Bhanu Prakash, Dadheech, Pankaj, Vibhute, Amol D., Sengan, Sudhakar
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 11.01.2024; Springer Nature B.V
• Subjects: 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

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...