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    A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

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    Date
    2021-10
    Author
    Munyua, John G.
    Wambugu, Geoffrey M
    Njenga, Stephen T.
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    Abstract
    Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.
    URI
    https://www.ijcit.com/index.php/ijcit/article/view/166
    https://www.scilit.net/article/dd7657f3ad67f0d3744ed9c8e2289b15
    http://hdl.handle.net/123456789/5547
    https://doi.org/10.24203/ijcit.v10i5.166
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    • Journal Articles (CI) [118]

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