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Published April 30, 2020 | Version v1
Journal article Open

A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM

  • 1. computer science department, Vellore Institute of Technology (VIT) University, Raipur, Chhattisgarh.
  • 2. school of computer science and engineering (SCOPE), Vellore Institute of Technology (VIT) University, Chennai, India.
  • 1. Publisher

Description

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.

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Journal article: 2249-8958 (ISSN)

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ISSN
2249-8958
Retrieval Number
D6476049420/2020©BEIESP