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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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 42; no. 6; pp. 1257 - 1272
Main Authors Sodemann, A. A., Ross, M. P., Borghetti, B. J.
Format Journal Article
LanguageEnglish
Published New-York, NY IEEE 01.11.2012
Institute of Electrical and Electronics Engineers
Subjects
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
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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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1094-6977
1558-2442
DOI:10.1109/TSMCC.2012.2215319