A Review of Anomaly Detection in Automated Surveillance
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 dev...
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| Published in | IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 42; no. 6; pp. 1257 - 1272 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New-York, NY
IEEE
01.11.2012
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1094-6977 1558-2442 |
| DOI | 10.1109/TSMCC.2012.2215319 |
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| 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. |
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| AbstractList | 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. |
| Author | Sodemann, A. A. Ross, M. P. Borghetti, B. J. |
| Author_xml | – sequence: 1 givenname: A. A. surname: Sodemann fullname: Sodemann, A. A. email: angela.sodemann@gmail.com organization: Coll. of Technol. & Innovation, Arizona State Univ., Phoenix, AZ, USA – sequence: 2 givenname: M. P. surname: Ross fullname: Ross, M. P. email: matthew.ross@us.af.mil organization: U.S. Air Force Acad., Colorado Springs, CO, USA – sequence: 3 givenname: B. J. surname: Borghetti fullname: Borghetti, B. J. email: brett.borghetti@afit.edu organization: Air Force Inst. of Technol., Dayton, OH, USA |
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| Keywords | Data analysis Extraction process Measurement sensor anomaly detection Pattern recognition Abnormal behavior machine learning Modeling Outlier behavior classification Surveillance Behavioral analysis Automatic measurement Classification Feature extraction Anomaly automated surveillance Learning algorithm Artificial intelligence Pattern extraction Monitoring |
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| SubjectTerms | Abnormal behavior Algorithms Anomalies anomaly detection Applied sciences Arrays Artificial intelligence Automated automated surveillance behavior classification Cameras Computer science; control theory; systems Cybernetics Data models Data processing. List processing. Character string processing Exact sciences and technology Feature extraction Learning machine learning Memory organisation. Data processing Sensor phenomena and characterization Software Surveillance Trajectory |
| Title | A Review of Anomaly Detection in Automated Surveillance |
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