Multi-sensor logical decision making in the single location surveillance point system

Mikko Nieminen, Tommi Räty, Mikko Lindholm

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    9 Citations (Scopus)

    Abstract

    As demand for surveillance of physical locations increases, automated decision making software can help maintain the rising costs of human monitoring. The variety in different types of sensors is also growing, and making use of their consolidated data can improve the decision making process. Using the constructive research method, we aim to define a design of a surveillance system's decision making component that utilizes data fusion from multiple types of sensors. As a solution we present the logical decision making server (LDMS), used in the single location surveillance point (SLSP), a system designed for monitoring an indoors location. The decision making capabilities in the LDMS are based on user-configurable security rules, which allow security personnel to define threats based on current and recent event reports from any or all of the environment&psila;s sensors. The LDMS has been successfully developed and integrated into an SLSP implementation.
    Original languageEnglish
    Title of host publicationProceedings of the 4th International Conference on Systems, ICONS 2009
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages86-90
    ISBN (Print)978-1-4244-3469-5, 978-0-7695-3551-7
    DOIs
    Publication statusPublished - 2009
    MoE publication typeA4 Article in a conference publication
    Event4th International Conference on Systems, ICONS 2009 - Gosier, France
    Duration: 1 Mar 20096 Mar 2009

    Conference

    Conference4th International Conference on Systems, ICONS 2009
    Abbreviated titleICONS 2009
    Country/TerritoryFrance
    CityGosier
    Period1/03/096/03/09

    Keywords

    • Multi-sensor surveillance
    • decision making
    • data fusion

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