5553791
doi
10.35940/ijeat.D6476.049420
oai:zenodo.org:5553791
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Publisher
Ranganathan Sridhar
school of computer science and engineering (SCOPE), Vellore Institute of Technology (VIT) University, Chennai, India.
A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM
Lovleen siddhu
computer science department, Vellore Institute of Technology (VIT) University, Raipur, Chhattisgarh.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Anomaly event detection, Autoencoder, LSTM, UCSD dataset, Convolutional neural networks.
<p>Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.</p>
Zenodo
2020-04-30
info:eu-repo/semantics/article
5553790
1633614510.779588
629924
md5:eb5c2ea551662933c7d506f45a78e461
https://zenodo.org/records/5553791/files/D6476049420.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
4
355-359
2020-04-30