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585 results for “Camera trap”

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dryad28/100

Camera traps Red deer exhibit spatial and temporal responses to hiking activity

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publicSep 2021View details →

dryad28/100

Assessing environmental DNA metabarcoding and camera trap surveys as complementary tools for biomonitoring of remote desert water bodies

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publicDec 2021View details →

dryad28/100

Data for: Estimation of density distribution in unmarked populations using camera traps

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publicFeb 2023View details →

zenodo24/100

Figure 1 in Camera traps capture images of predators of Caiman crocodilus yacare eggs (Reptilia: Crocodylia) in Brazil's Pantanal wetlands

Figure 1. Camera trap installed next to a Pantanal caiman (Caiman crocodilus yacare) nest, Brazil.

opencc-by-4.0Jun 2014View details →

dryad24/100

Data from: Machine learning to classify animal species in camera trap images: applications in ecology

Motion‐activated cameras ("camera traps") are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out‐of‐sample (or "out‐of‐distribution" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out‐of‐sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out‐of‐sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non‐invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.

opencc-zeroDec 2018View details →

zenodo24/100

Dataset animal-vs-empty collected by camera trap prototype for research purposes

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opencc-by-4.0Dec 2023View details →

zenodo24/100

Fig. 5 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania

Fig. 5. Seasonal variation of otter activity pattern in study area during March 2011–April 2016.

opencc-by-4.0Jan 2019View details →

zenodo24/100

Figure 6 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554

Figure 6 Continued.

opencc-by-4.0Jul 2018View details →

zenodo24/100