Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks-Reference-Cited by-同舟云学术

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

Published:2024-01-11 Issue:1 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Almahadin Ghayth,Subburaj Maheswari^ORCID,Hiari Mohammad,Sathasivam Singaram Saranya,Kolla Bhanu Prakash,Dadheech Pankaj,Vibhute Amol D.,Sengan Sudhakar

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-02542-1.pdf

Reference61 articles.

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2. Kantipudi MVVP, Kumar S, Jha AK. Scene text recognition based on bidirectional LSTM and deep neural network. Comput Intell Neurosci. 2021;2021:1–11.

3. Ratnam DV, Rao KN. Bi-LSTM based deep learning method for 5G signal detection and channel estimation. AIMS Electron Electr Eng. 2021;5(4):334–41.

4. Reddybattula KD, et al. Ionospheric TEC forecasting over an Indian low latitude location using long short-term memory (LSTM) deep learning network. Universe. 2022;8(11):562.

5. Enireddy V, Karthikeyan C, Babu DV. OneHotEncoding and LSTM-based deep learning models for protein secondary structure prediction. Soft Comput. 2022;26(8):3825–36.

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