Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence

• URL: http://arxiv.org/abs/2202.02283v1
• Date: Fri, 4 Feb 2022 18:13:09 GMT
• Title: Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence
• Authors: Juliana V\'elez, Paula J. Castiblanco-Camacho, Michael A. Tabak, Carl Chalmers, Paul Fergus and John Fieberg
• Abstract summary: Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions. The potential of Artificial Intelligence (AI), specifically Deep Learning (DL), to process camera-trap data has gained considerable attention. Using DL for these applications involves training algorithms, such as Convolutional Neural Networks (CNNs) to automatically detect objects and classify species.
• Score: 0.18350044465969417
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions. Although camera traps provide a cost-effective method for monitoring species, the time required for data processing can limit survey efficiency. Thus, the potential of Artificial Intelligence (AI), specifically Deep Learning (DL), to process camera-trap data has gained considerable attention. Using DL for these applications involves training algorithms, such as Convolutional Neural Networks (CNNs), to automatically detect objects and classify species. To overcome technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate DL in easy-to-use interfaces. We review key characteristics of four AI-powered platforms --Wildlife Insights (WI), MegaDetector (MD), Machine Learning for Wildlife Image Classification (MLWIC2), and Conservation AI-- including data management tools and AI features. We also provide R code in an open-source GitBook, to demonstrate how users can evaluate model performance, and incorporate AI output in semi-automated workflows. We found that species classifications from WI and MLWIC2 generally had low recall values (animals that were present in the images often were not classified to the correct species). Yet, the precision of WI and MLWIC2 classifications for some species was high (i.e., when classifications were made, they were generally accurate). MD, which classifies images using broader categories (e.g., "blank" or "animal"), also performed well. Thus, we conclude that, although species classifiers were not accurate enough to automate image processing, DL could be used to improve efficiencies by accepting classifications with high confidence values for certain species or by filtering images containing blanks.

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