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Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Westworth, Sally OA; Chalmers, Carl; Fergus, Paul; Longmore, Steven N; Piel, Alex K; Wich, Serge A; (2022) Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning. Sensors , 22 (14) , Article 5386. 10.3390/s22145386. Green open access

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Abstract

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation.

Type: Article
Title: Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/s22145386
Publisher version: https://doi.org/10.3390/s22145386
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: citizen scientists; poacher; wildlife; biodiversity conservation; automation
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Anthropology
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10153059
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