IJSS_Behaviour.pdf (6.79 MB)

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

Download (6.79 MB)
journal contribution
posted on 14.10.2014, 14:55 by Hyondong Oh, Seungkeun Kim, Hyo-Sang Shin, Antonios Tsourdos, Barry A. White
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.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Journal of Systems Science

Volume

45

Issue

12

Pages

2499 - 2514

Citation

OH, H. ... et al, 2014. Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles. International Journal of Systems Science, 45 (12), pp. 2499 - 2514.

Publisher

© Taylor and Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2014

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Systems Science on 4 Mar 2013, available online at: http://www.tandfonline.com/10.1080/00207721.2013.772677

ISSN

0020-7721

Language

en

Exports