Adaptive behaviour for a self-organising video surveillance system using a genetic algorithm

Fabrice Saffre (Corresponding Author), Hanno Hildmann

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    Abstract

    Genetic algorithms (GA’s) are mostly used as an offline optimisation method to discover a suitable solution to a complex problem prior to implementation. In this paper, we present a different application in which a GA is used to progressively adapt the collective performance of an ad hoc collection of devices that are being integrated post-deployment. Adaptive behaviour in the context of this article refers to two dynamic aspects of the problem: (a) the availability of individual devices as well as the objective functions for the performance of the entire population. We illustrate this concept in a video surveillance scenario in which already installed cameras are being retrofitted with networking capabilities to form a coherent closed-circuit television (CCTV) system. We show that this can be conceived as a multi-objective optimisation problem which can be solved at run-time, with the added benefit that solutions can be refined or modified in response to changing priorities or even unpredictable events such as faults. We present results of a detailed simulation study, the implications of which are being discussed from both a theoretical and practical viewpoint (trade-off between saving computational resources and surveillance coverage).

    Original languageEnglish
    Article number74
    JournalAlgorithms
    Volume14
    Issue number3
    DOIs
    Publication statusPublished - 2021
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Adaptive scheduling
    • Camera surveillance
    • Dynamic objective function
    • Genetic algorithm
    • Optimisation
    • Run-time

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