Westworth, SOA, Chalmers, C, Fergus, P, Longmore, SN, Piel, AK and Wich, SA (2022) Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning. Sensors, 22 (14). p. 5386.

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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.

Item Type:	Article
Uncontrolled Keywords:	0502 Environmental Science and Management; 0602 Ecology; 0301 Analytical Chemistry; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QB Astronomy Q Science > QC Physics Q Science > QH Natural history Q Science > QH Natural history > QH301 Biology
Divisions:	Astrophysics Research Institute Biological & Environmental Sciences (from Sep 19) Computer Science & Mathematics
Publisher:	MDPI AG
SWORD Depositor:	A Symplectic
Date Deposited:	20 Jul 2022 09:44
Last Modified:	20 Jul 2022 09:45
DOI or ID number:	10.3390/s22145386
URI:	https://researchonline.ljmu.ac.uk/id/eprint/17256

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