Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation. 

/pdf/perspectives-in-machine-learning-for-wildlife-conservation-2rf4us0n.pdf

Perspectives in machine learning for wildlife conservation

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development. 

/pdf/multi-animal-pose-estimation-identification-and-tracking-3rakqumm.pdf

Multi-animal pose estimation, identification and tracking with DeepLabCut

/pdf/multi-animal-pose-estimation-identification-and-tracking-1ipccgud.pdf

Quantification of behaviours of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyse the behaviour of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behaviour—even in complex environments directly from raw video frames—that requires no intervention after initial human supervision. Our behavioural classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation and classification of complex behaviour, outperforming the state of the art. SIPEC successfully recognizes multiple behaviours of freely moving individual mice as well as socially interacting non-human primates in three dimensions, using data only from simple mono-vision cameras in home-cage set-ups. The use of deep neural networks for the automated analysis of behavioural videos has emerged as a tool in neuroscience, medicine and psychology. Marks and colleagues present a pipeline capable of tracking and identifying animals, as well as classifying individual and interacting animal behaviour in video recordings and even in complex environments. 

/pdf/deep-learning-based-identification-tracking-pose-estimation-tivhbhym.pdf

Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments

Abstract The iNaturalist platform generates millions of research-grade biodiversity records via a system in which users collectively reach consensus on taxonomic identification. In the present article, we examine how identifiers and their efforts, an understudied component of the platform, support data generation. Identification is keeping pace with rapid growth of observations, assisted by a small subset of highly active users who tend to be taxonomically specialized. Identifier experience is the primary determinant of whether records reach research grade, and the time it takes to do so. Time to reach research grade has fallen rapidly with growing identification effort and use of computer vision, and research-grade identifications are generally stable. Most observations are vetted by experienced identifiers, although identifications are not free of biases. We close by providing suggestions for enhanced identification quality and continuing steps to enhance equitable credit and trust across the ecosystem of observers, identifiers, and data users. 

Identifying the identifiers: How iNaturalist facilitates collaborative, research-relevant data generation and why it matters for biodiversity science

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future.

Perspectives on Individual Animal Identification from Biology and Computer Vision.

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.

Perspectives on individual animal identification from biology and computer vision

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Perspectives on Individual Animal Identification from Biology and Computer Vision.

Perspectives on individual animal identification from biology and computer vision