A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM
Lovleen siddhu1, Ranganathan Sridhar2

1Lovleen Siddhu*, computer science department, Vellore Institute of Technology (VIT) University, Raipur, Chhattisgarh.
2Ranganathan Sridhar, school of computer science and engineering (SCOPE), Vellore Institute of Technology (VIT) University, Chennai, India.

Manuscript received on April 05, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on April 30, 2020. | PP: 355-359 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6476049420/2020©BEIESP | DOI: 10.35940/ijeat.D6476.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Anomaly event detection, Autoencoder, LSTM, UCSD dataset, Convolutional neural networks.