Anomaly Detection in Surveillance Videos Using Deep Learning

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

• Publication year

  2022

• Venue

  2022 International Conference on Knowledge Engineering and Communication Systems (ICKES)

• Publication date

  2022-12-28

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICKECS56523.2022.10059844
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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