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Improving the precision and accuracy of animal population estimates with aerial image object detection

1. Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. 2. Aerial counts using cost‐efficient Unmanned Aerial Vehicles or microlight aircraft with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi‐class convolutional neural

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Automatic animal identification from drone camera based on point pattern analysis of herd behaviour

This study investigated the accuracy of animal identification based on herd behaviour from drone camera footage. We evaluated object detection algorithms and point pattern analysis, using footage from drone altitudes ranging from 15 m to 130 m. We applied transfer learning to state-of-the-art lightweight object detection algorithms (Tensorflow and YOLO) based on feature extraction. In the point pattern analysis, we treated each animal as a point and identified them by the behavioural pattern of those points. The five animal species

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Metric Learning Based Pattern Matching For Species Agnostic Animal Re-Identification

In the active effort to monitor and protect endangered animal species, modern technology is replacing the previously used conventional techniques of tracking using GPS or tagging which are considered invasive in nature. The non-invasive technology such as camera traps collects a large amount of data remotely, enabling the use of computer vision techniques to perform the analysis including re-identification of animal individuals. The re-identification of the animal individuals can be done by training a convolutional neural network to measure the

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An automated program to find animals and crop photographs for individual recognition

Detailed data on individual animals are critical to ecological and evolutionary studies, but attaching identifying marks can alter individual fates and behavior leading to biases in parameter estimates and ethical issues. Individual-recognition software has been developed to assist in identifying many species from non-invasive photographic data. These programs utilize algorithms to find unique individual characteristics and compare images to a catalogue of known individuals. Currently, all applications for individual identification require manual processing to crop images so only the area

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