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Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

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Listed:
  • Hyondong Oh
  • Seungkeun Kim
  • Hyo-Sang Shin
  • Antonios Tsourdos
  • Brian A. White

Abstract

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.

Suggested Citation

  • Hyondong Oh & Seungkeun Kim & Hyo-Sang Shin & Antonios Tsourdos & Brian A. White, 2014. "Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 2499-2514, December.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:12:p:2499-2514
    DOI: 10.1080/00207721.2013.772677
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    References listed on IDEAS

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    1. Paolo Gunetti & Haydn Thompson & Tony Dodd, 2013. "Autonomous mission management for UAVs using soar intelligent agents," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(5), pages 831-852.
    2. Chakroborty, Partha, 2006. "Models of vehicular traffic: An engineering perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(1), pages 151-161.
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