Optimisation of surveillance camera site locations and viewing angles using a novel multi-attribute, multi-objective genetic algorithm: A day/night anti-poaching application

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The optimisation of surveillance and detection systems comprised of specialised cameras is a well-known problem in the operations research literature. In these problems, the aim is to locate optimal camera sites so that their combined coverage with respect to some area of interest – called a cover zone – is maximised. The standard approach is to maximise cover with respect to a single cover zone, and to consider either cameras providing rotational (360°) cover, or cameras fixed to a specific direction and with visibility limited to within the camera's field-of-view. The Rhino Pride Foundation in South Africa required the optimisation of a camera surveillance system for a new protected area. Their coverage requirements were, however, beyond what has been previously encountered in the literature. Four covering objectives over three separate cover zones were to be maximised, while the system was to be optimised for rotational cover during the day, and some cameras would be required to be fixed towards a high-risk zone at night and limited to their field-of-view. A novel multi-attribute genetic algorithm based on the popular NSGA-II was developed for this purpose. Various solutions were provided to and considered by the Rhino Pride Foundation, and the final selected solution resulted in camera site locations providing high-quality cover with respect to all the covering objectives, while requiring fewer cameras than initially expected – resulting in significant cost savings and reduced future maintenance and upgrade requirements. The solution approach presented here may be applied to other site-selection problems with similar coverage requirements, including military radar and weapon systems, and wildfire detection systems.
Original languageEnglish
Article number101638
Peer-reviewed scientific journalComputers, Environment and Urban Systems
Volume88
Number of pages23
ISSN0198-9715
DOIs
Publication statusPublished - 23.04.2021
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Anti-poaching
  • Surveillance
  • Maximal cover
  • Backup cover
  • Genetic algorithm

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoHP: Humanitarian and societal logistics

Fingerprint

Dive into the research topics of 'Optimisation of surveillance camera site locations and viewing angles using a novel multi-attribute, multi-objective genetic algorithm: A day/night anti-poaching application'. Together they form a unique fingerprint.

Cite this