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

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.