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

Authors

  • John Gatara Munyua John Gatara Munyua School of Computing and Information Technology, Murang’a University of Technology Murang’a, Kenya
  • Geoffrey Mariga Wambugu School of Computing and Information Technology, Murang’a University of Technology Murang’a, Kenya
  • Stephen Thiiru Njenga School of Computing and Information Technology, Murang’a University of Technology Murang’a, Kenya

DOI:

https://doi.org/10.24203/ijcit.v10i5.166

Keywords:

Deep Learning, Anomaly Detection, Anomaly Detection in Videos, Intelligence Video Surveillance, Deep Anomaly Detection

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.

Author Biographies

Geoffrey Mariga Wambugu, School of Computing and Information Technology, Murang’a University of Technology Murang’a, Kenya

Dean School of Computing and Information Technology

Murang'a University of Technology

Stephen Thiiru Njenga, School of Computing and Information Technology, Murang’a University of Technology Murang’a, Kenya

Senior Lecturer

Department of Computer Science

Murang'a University of Technology

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Published

2021-10-22

How to Cite

Munyua, J. G., Wambugu, G. M., & Njenga, S. T. (2021). A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos. International Journal of Computer and Information Technology(2279-0764), 10(5). https://doi.org/10.24203/ijcit.v10i5.166