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Sensors 2015, 15(12), 31362-31391; doi:10.3390/s151229861

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers

1
Interdisciplinary Centre for Security, Reliability and Trust, SnT - University of Luxembourg, 4 Rue Alphonse Weicker, L-2721 Luxembourg, Luxembourg
2
Centre for Automation and Robotics (CAR), Universidad Politécnica de Madrid (UPM-CSIC), Calle de José Gutiérrez Abascal 2, 28006 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Gonzalez Toro
Received: 28 September 2015 / Revised: 30 November 2015 / Accepted: 2 December 2015 / Published: 12 December 2015
(This article belongs to the Special Issue UAV Sensors for Environmental Monitoring)

Abstract

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing. View Full-Text
Keywords: unmanned aerial vehicles; computer vision; animal tracking; face detection; vision-based control; object following; autonomous navigation; autonomous landing; anti-poaching unmanned aerial vehicles; computer vision; animal tracking; face detection; vision-based control; object following; autonomous navigation; autonomous landing; anti-poaching
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Olivares-Mendez, M.A.; Fu, C.; Ludivig, P.; Bissyandé, T.F.; Kannan, S.; Zurad, M.; Annaiyan, A.; Voos, H.; Campoy, P. Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers. Sensors 2015, 15, 31362-31391.

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