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Automated visitor and wildlife monitoring with camera traps and machine learning

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007612
URN to cite this document: urn:nbn:de:bvb:703-epub-7612-6

Title data

Mitterwallner, Veronika ; Peters, Anne ; Edelhoff, Hendrik ; Mathes, Gregor H. ; Nguyen, Hien ; Peters, Wibke ; Heurich, Marco ; Steinbauer, Manuel:
Automated visitor and wildlife monitoring with camera traps and machine learning.
In: Remote Sensing in Ecology and Conservation. (2023) .
ISSN 2056-3485
DOI der Verlagsversion: https://doi.org/10.1002/rse2.367

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Project information

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Integrative evaluations of the effects of recreational use on wildlife as a basis for evidence-based visitor management.
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Project financing: Deutsche Forschungsgemeinschaft
Bayerisches Staatsministerium für Umwelt und Verbraucherschutz

Abstract

As human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large-scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open-source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open-source object detection model MegaDetector in cross-regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long-term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross-regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long-term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.

Further data

Item Type: Article in a journal
Keywords: camera traps; human–wildlife interactions; machine learning; recreation ecology; wildlifeecology
DDC Subjects: 500 Science > 500 Natural sciences
Institutions of the University: Faculties > Faculty of Cultural Studies > Department of Sport Science > Professor Sport Ecology
Faculties > Faculty of Cultural Studies > Department of Sport Science > Professor Sport Ecology > Professor Sport Ecology - Univ.-Prof. Dr. Manuel Jonas Steinbauer
Research Institutions > Central research institutes > Bayreuth Center of Ecology and Environmental Research- BayCEER
Research Institutions > Central research institutes > Bayreuth Center of Sport Science (BaySpo)
Faculties
Faculties > Faculty of Cultural Studies
Faculties > Faculty of Cultural Studies > Department of Sport Science
Research Institutions
Research Institutions > Central research institutes
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7612-6
Date Deposited: 20 Mar 2024 12:26
Last Modified: 20 Mar 2024 12:27
URI: https://epub.uni-bayreuth.de/id/eprint/7612

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