Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Westworth, Sally O. A., Chalmers, Carl, Fergus, Paul, Longmore, Steven N., Piel, Alex K., Wich, Serge A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784755620245667840
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
work_keys_str_mv AT westworthsallyoa understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning
AT chalmerscarl understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning
AT ferguspaul understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning
AT longmorestevenn understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning
AT pielalexk understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning
AT wichsergea understandingexternalinfluencesontargetdetectionandclassificationusingcameratrapimagesandmachinelearning