Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm

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).

Keywords

• adaptive scheduling
• genetic algorithm
• dynamic objective function
• camera surveillance
• optimisation
• run-time