Next Article in Journal
Combining Orchardgrass and Alfalfa: Effects of Forage Ratios on In Vitro Rumen Degradation and Fermentation Characteristics of Silage Compared with Hay
Next Article in Special Issue
Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence
Previous Article in Journal
Effects of Dietary Vegetable Oils on Mammary Lipid-Related Genes in Holstein Dairy Cows
Previous Article in Special Issue
Activity Rhythms of Coexisting Red Serow and Chinese Serow at Mt. Gaoligong as Identified by Camera Traps
Open AccessArticle

ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images

1
School of Science and Technology, University of New England, Armidale, NSW 2351, Australia
2
School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
3
Vertebrate Pest Research Unit, NSW Department of Primary Industries, Allingham St, Armidale, NSW 2351, Australia
4
Vertebrate Pest Research Unit, NSW Department of Primary Industries, 1447 Forest Road, Orange, NSW 2800, Australia
5
Manaaki Whenua—Landcare Research, Private Bag 92170, Auckland 1142, New Zealand
6
IO Design Australia, Armidale, NSW 2350, Australia
7
Vertebrate Pest Research Unit, NSW Department of Primary Industries, PO Box 530, Coffs Harbour, NSW 2450, Australia
*
Author to whom correspondence should be addressed.
Animals 2020, 10(1), 58; https://doi.org/10.3390/ani10010058
Received: 1 November 2019 / Revised: 13 December 2019 / Accepted: 20 December 2019 / Published: 27 December 2019
(This article belongs to the Special Issue The Application of Camera Trap Technology in Field Research)
Camera trap wildlife surveys can generate vast amounts of imagery. A key problem in the wildlife ecology field is that vast amounts of time is spent reviewing this imagery to identify the species detected. Valuable resources are wasted, and the scale of studies is limited by this review process. The use of computer software capable of extracting false positives, automatically identifying animals detected and sorting imagery could greatly increase efficiency. Artificial intelligence has been demonstrated as an effective option for automatically identifying species from camera trap imagery. Currently available code bases are inaccessible to the majority of users; requiring high-performance computers, advanced software engineering skills and, often, high-bandwidth internet connections to access cloud services. The ClassifyMe software tool is designed to address this gap and provides users the opportunity to utilise state-of-the-art image recognition algorithms without the need for specialised computer programming skills. ClassifyMe is especially designed for field researchers, allowing users to sweep through camera trap imagery using field computers instead of office-based workstations.
We present ClassifyMe a software tool for the automated identification of animal species from camera trap images. ClassifyMe is intended to be used by ecologists both in the field and in the office. Users can download a pre-trained model specific to their location of interest and then upload the images from a camera trap to a laptop or workstation. ClassifyMe will identify animals and other objects (e.g., vehicles) in images, provide a report file with the most likely species detections, and automatically sort the images into sub-folders corresponding to these species categories. False Triggers (no visible object present) will also be filtered and sorted. Importantly, the ClassifyMe software operates on the user’s local machine (own laptop or workstation)—not via internet connection. This allows users access to state-of-the-art camera trap computer vision software in situ, rather than only in the office. The software also incurs minimal cost on the end-user as there is no need for expensive data uploads to cloud services. Furthermore, processing the images locally on the users’ end-device allows them data control and resolves privacy issues surrounding transfer and third-party access to users’ datasets. View Full-Text
Keywords: camera traps; camera trap data management; deep learning; ecological software; species recognition; wildlife monitoring camera traps; camera trap data management; deep learning; ecological software; species recognition; wildlife monitoring
Show Figures

Figure 1

MDPI and ACS Style

Falzon, G.; Lawson, C.; Cheung, K.-W.; Vernes, K.; Ballard, G.A.; Fleming, P.J.S.; Glen, A.S.; Milne, H.; Mather-Zardain, A.; Meek, P.D. ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images. Animals 2020, 10, 58.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop