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 October 31, 2025, https://repository.horizon.ac.id/items/show/9006.