期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2021
卷号：10
期号：5
页码：1-8
语种：English
出版社：International Journal of Computer and Information Technology
摘要：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.
关键词：Deep Learning;Anomaly Detection;Anomaly Detection in Videos;Intelligence Video Surveillance;Deep Anomaly Detec
