A Review of Anomaly Detection in Automated Surveillance
Abstract
As surveillance becomes ubiquitous, the amount of data to be processed grows along with the demand for manpower to interpret the data. A key goal of surveillance is to detect behaviors that can be considered anomalous. As a result, an extensive body of research in automated surveillance has been developed, often with the goal of automatic detection of anomalies. Research into anomaly detection in automated surveillance covers a wide range of domains, employing a vast array of techniques. This review presents an overview of recent research approaches on the topic of anomaly detection in automated surveillance. The reviewed studies are analyzed across five aspects: surveillance target, anomaly definitions and assumptions, types of sensors used and the feature extraction processes, learning methods, and modeling algorithms.
- Publication:
-
IEEE Transactions on Human-Machine Systems
- Pub Date:
- 2012
- DOI:
- Bibcode:
- 2012ITHMS..42.1257S
- Keywords:
-
- Abnormal behavior;
- anomaly detection;
- automated surveillance;
- behavior classification;
- machine learning