Abstract Recent advancements in the automatic re-identification of animal individuals from images have opened up new possibilities for studying wildlife through camera traps and citizen science projects. Existing methods leverage distinct and permanent visual body markings, such as fur patterns or scars, and typically employ one of two approaches: local features or end-to-end learning. The end-to-end learning-based methods outperform local feature-based methods given a sufficient amount of good-quality training data, but the challenge of gathering such datasets for wildlife animals means that local feature-based methods remain a more practical approach for many species. In this study, we aim to achieve two goals: (1) to obtain a better understanding of the impact of training-set size on animal re-identification, and (2) to explore ways to combine various methods to leverage the advantages of their approaches for re-identification. In the work, we conduct comprehensive experiments across six different methods and six animal species with various training set sizes. Furthermore, we propose a simple yet effective combination strategy and show that a properly selected method combinations outperform the individual methods with both small and large training sets up to 30%. Additionally, the proposed combination strategy offers a generalizable framework to improve accuracy across species and address the challenges posed by small datasets, which are common in ecological research. This work lays the foundation for more robust and accessible tools to support wildlife conservation, population monitoring, and behavioral studies.

ABSTRACT The Grévy's zebra, an endangered species native to Kenya and southern Ethiopia, has been the target of sustained conservation efforts in recent years. Accurately monitoring Grévy's zebra populations is essential for ecologists to evaluate ongoing conservation initiatives. Recently, in both 2016 and 2018, a full census of the Grévy's zebra population was enabled by the Great Grévy's Rally (GGR), a citizen science event that combines teams of volunteers to capture data with computer vision algorithms that help experts estimate the number of individuals in the population. A complementary, scalable, cost‐effective and long‐term Grévy's population monitoring approach involves deploying a network of camera traps, which we have done at the Mpala Research Centre in Laikipia County, Kenya. In both scenarios, a substantial majority of the images of zebras are not usable for individual identification due to ‘in‐the‐wild’ imaging conditions—occlusions from vegetation or other animals, oblique views, low image quality and animals that appear in the far background and are thus too small to identify. Camera trap images, without an intelligent human photographer to select the framing and focus on the animals of interest, are of even poorer quality, with high rates of occlusion and high spatiotemporal similarity within image bursts. We employ an image filtering pipeline incorporating animal detection, species identification, viewpoint estimation, quality evaluation and temporal subsampling to compensate for these factors and obtain individual crops from camera trap and GGR images of suitable quality for re‐ID. We then employ the local clusterings and their alternatives (LCA) algorithm, a hybrid computer vision and graph clustering method for animal re‐ID, on the resulting high‐quality crops. Our method processed images taken during GGR‐16 and GGR‐18 in Meru County, Kenya, into 4142 highly comparable annotations, requiring only 120 contrastive same‐vs‐different‐individual decisions from a human reviewer to produce a population estimate of 349 individuals (within 4.6 of the ground truth count in Meru County). Our method also efficiently processed 8.9M unlabelled camera trap images from 70 camera traps at Mpala over 2 years into 685 encounters of 173 unique individuals, requiring only 331 contrastive decisions from a human reviewer.

This paper addresses the problem of identifying individual animals in images based on extracting and matching contours, focusing in particular on the trailing edges of humpback whale flukes and the outline of the ears of African savanna elephants. A coarse-grained FCNN is learned to isolate the contour in an image, and a fine-grained FCNN is learned to provide more precise boundary information. The latter is trained by generating synthetic boundaries from coarse, easily-extracted training data, avoiding tedious manual effort. An A* algorithm extracts the final contour, which is converted to set of digital curvature descriptors and matched against a database of descriptors using local-naive Bayes nearest neighbors. We show that using the learned fine-grained FCNN produces more accurate contours than using image gradients for fine localization, especially for elephant ears where the boundaries are primarily texture. Matching using contours extracted using the fine-grained FCNN improves top-1 accuracy from 80% to 85% for flukes and 78% to 84% for ears.

This paper proposes a 5-component detection pipeline for use in a computer vision-based animal recognition system. The end result of our proposed pipeline is a collection of novel annotations of interest (AoI) with species and view-point labels. These AoIs, for example, could be fed as the focused input data into an appearance-based animal identification system. The goal of our method is to increase the reliability and automation of animal censusing studies and to provide better ecological information to conservationists. Our method is able to achieve a localization mAP of 81.67%, a species and viewpoint annotation classification accuracy of 94.28% and 87.11%, respectively, and an AoI accuracy of 72.75% across 6 animal species of interest. We also introduce the Wildlife Image and Localization Dataset (WILD), which contains 5,784 images and 12,007 labeled annotations across 28 classification species and a variety of challenging, real-world detection scenarios.