A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos
Dublin Core
Title
A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos
Subject
Deep Learning; Anomaly Detection; Anomaly Detection in Videos; Intelligence Video Surveillance; Deep Anomaly Detection.
Description
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.
Creator
John Gatara Munyua, Geoffrey Mariga Wambugu, Stephen Thiiru Njenga
Source
www.ijcit.com
Date
September 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
John Gatara Munyua, Geoffrey Mariga Wambugu, Stephen Thiiru Njenga, “A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos,” Repository Horizon University Indonesia, accessed May 31, 2025, https://repository.horizon.ac.id/items/show/9006.