Nature Conservation Drones for Automatic Localization and Counting of Animals

Abstract

General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract. This paper is concerned with nature conservation by automatically monitoring animal distribution and animal abundance. Typically, such conservation tasks are performed manually on foot or after an aerial recording from a manned aircraft. Such manual approaches are expensive, slow and labor intensive. In this paper, we investigate the combination of small unmanned aerial vehicles (UAVs or "drones") with automatic object recognition techniques as a viable solution to manual animal surveying. Since no controlled data is available, we record our own animal conservation dataset with a quadcopter drone. We evaluate two nature conservation tasks: i) animal detection ii) animal counting using three state-of-the-art generic object recognition methods that are particularly well-suited for on-board detection. Results show that object detection techniques for human-scale photographs do not directly translate to a drone perspective, but that light-weight automatic object detection techniques are promising for nature conservation tasks.

Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?

“…Several of the above-cited studies attempted, with varying degrees of success, various digital image analysis techniques for automated detection and (or) counting of animals (Abd-Elrahman et al 2005;Selby et al 2011;Chabot and Bird 2012;Grenzdorffer 2013;Maire et al 2013;Christiansen et al 2014;van Gemert et al 2015). Numerous additional examples of similar automated analysis techniques applied to imagery acquired by conventional aircraft are available in the broader wildlife literature (e.g., Laliberte and Ripple 2003;Descamps et al 2011;Groom et al 2013).…”

“…Finally, UAS have detected orangutan (Pongo abelii) nests in treetops (Koh and Wich 2012;van Gemert et al 2015) as well as northern pocket gopher (Thomomys talpoides) mounds in grasslands . Christiansen et al (2014) demonstrated automatic detection of rabbits in thermal imagery captured from a lift, intended to simulate a low-flying UAS scanning for small concealed animals in the path of agricultural machinery.…”

“…Other efforts have focused on African and Southeast Asian megafauna, namely, elephants (Elephas maximus and Loxodonta africana) (Koh and Wich 2012;Vermeulen et al 2013), rhinoceros (Diceros bicornis and Ceratotherium simum) and giraffes (Giraffa camelopardalis), including in the context of anti-poaching efforts (Mulero-Pazmany et al 2014b). Van Gemert et al (2015) developed image analysis techniques aimed at automatically detecting and counting these animals in UAS imagery.…”

An Economical Custom-Built Drone for Assessing Whale Health

“…In the terrestrial world, UAVs have been used for a wide variety of conservation applications (van Gemert et al, 2014;Gonzalez et al, 2016). Some examples include, counting elephants (Loxodonta africana) (Linchant et al, 2013;Vermeulen et al, 2013), UAV surveillance (anti-poaching tools) for elephants and rhinoceros (Diceros bicornis and Ceratotherium simum) (Marks, 2014;MuleroPázmány et al, 2014;Hahn et al, 2017), locating chimpanzee nests (Pan troglodytes) (van Andel et al, 2015) and mapping Sumatran orangutan (Pongo abelii) habitat, distribution and density Szantoi et al, 2017).…”

Tracking of Animals Using Airborne Cameras

“…[56,87,110,119] for a selection of such projects. A project targeted directly at anti-poaching of rhinoceros is presented in [90], where an unmanned aerial system was deployed in the KwaZulu-Natal province in South Africa.…”