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Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning
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, su...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319727/ https://www.ncbi.nlm.nih.gov/pubmed/35891075 http://dx.doi.org/10.3390/s22145386 |
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author | Westworth, Sally O. A. Chalmers, Carl Fergus, Paul Longmore, Steven N. Piel, Alex K. Wich, Serge A. |
author_facet | Westworth, Sally O. A. Chalmers, Carl Fergus, Paul Longmore, Steven N. Piel, Alex K. Wich, Serge A. |
author_sort | Westworth, Sally O. A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9319727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93197272022-07-27 Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning Westworth, Sally O. A. Chalmers, Carl Fergus, Paul Longmore, Steven N. Piel, Alex K. Wich, Serge A. Sensors (Basel) Article 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. MDPI 2022-07-19 /pmc/articles/PMC9319727/ /pubmed/35891075 http://dx.doi.org/10.3390/s22145386 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Westworth, Sally O. A. Chalmers, Carl Fergus, Paul Longmore, Steven N. Piel, Alex K. Wich, Serge A. Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title | Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title_full | Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title_fullStr | Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title_full_unstemmed | Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title_short | Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning |
title_sort | understanding external influences on target detection and classification using camera trap images and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319727/ https://www.ncbi.nlm.nih.gov/pubmed/35891075 http://dx.doi.org/10.3390/s22145386 |
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