A review of anomaly detection in automated surveillance

Angela A. Sodemann, Matthew P. Ross, Brett J. Borghetti

    Research output: Contribution to journalArticlepeer-review

    189 Scopus citations


    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 languageEnglish (US)
    Article number6392472
    Pages (from-to)1257-1272
    Number of pages16
    JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
    Issue number6
    StatePublished - 2012


    • 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


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