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Closing the gap between camera trap software development and the user community
Author(s) -
Thomson Robert,
Potgieter Gail C.,
Bahaaeldin Laila
Publication year - 2018
Publication title -
african journal of ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.499
H-Index - 54
eISSN - 1365-2028
pISSN - 0141-6707
DOI - 10.1111/aje.12550
Subject(s) - software , flexibility (engineering) , computer science , camera trap , standardization , automation , trap (plumbing) , software engineering , software development , world wide web , data science , human–computer interaction , engineering , operating system , mechanical engineering , population , statistics , demography , mathematics , environmental engineering , sociology
Like many technological advances in the modern era, camera traps present both an exciting opportunity and a host of unforeseen challenges. One critical challenge is processing the large numbers of images/videos from camera traps efficiently and accurately such that resultant data can be analysed, stored and shared with others. Recognising this need, several biologist teams have developed software to meet their own project requirements, but no one package has been developed to meet the diverse array of requirements of the global research community. We combined an online user survey ( N = 67) and literature review to create a list of 42 desired features in five categories. We tested six freely available specialist camera trap software and three nonspecialist image viewer software packages against these criteria. Users were most interested in data processing efficiency and automation features (37 of 84 requests). Seven user‐requested features were not available in any of the software we tested. Thirty‐two unique requests indicated the importance of software flexibility, contrasting with growing calls for standardization in the literature. We suggest involving the global user community, current software developers and computer scientists in an inclusive, planned approach to addressing arguably the greatest challenge facing camera trap‐based research today.