Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence
Department of Anthropology, Durham University, Durham DH1 3LE, UK
Conservation Ecology Group, Department of Biosciences, Durham University, Durham DH1 3LE, UK
The Society for Preservation of Endangered Carnivores and Their International Ecological Study (SPECIES), Ventura, CA 93006, USA
Author to whom correspondence should be addressed.
Received: 2 December 2019 / Revised: 9 January 2020 / Accepted: 10 January 2020 / Published: 14 January 2020
Camera traps, also known as “game cameras” or “trail cameras”, have increasingly been used in wildlife research over the last 20 years. Although early units were bulky and the set-up was complicated, modern camera traps are compact, integrated units able to collect vast digital datasets. Some of the challenges now facing researchers include the time required to view, classify, and sort all of the footage collected, as well as the logistics of establishing and maintaining camera trap sampling arrays across wide geographic areas. One solution to this problem is to enlist or recruit the public for help as ‘citizen scientists’ collecting and processing data. Artificial Intelligence (AI) is also being used to identify animals in digital photos and video; however, this process is relatively new, and machine-based classifications are not yet fully reliable. By combining citizen science with AI, it should be possible to improve efficiency and increase classification accuracy, while simultaneously maintaining and promoting the benefits associated with public engagement with, and awareness of, wildlife.