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.
Original language | English (US) |
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Article number | 6392472 |
Pages (from-to) | 1257-1272 |
Number of pages | 16 |
Journal | IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews |
Volume | 42 |
Issue number | 6 |
DOIs | |
State | Published - 2012 |
Keywords
- Abnormal behavior
- anomaly detection
- automated surveillance
- behavior classification
- machine learning
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering