CNN Based Flank Predictor for Quadruped Animal Species

TL;DR

This paper develops a CNN-based flank predictor for quadruped animals, improving individual identification accuracy by automatically predicting visible flanks using transfer learning on existing datasets.

Contribution

It introduces a transfer learning approach to train CNN models for flank prediction, utilizing datasets originally labeled for pose estimation, and evaluates in real-world scenarios.

Findings

Achieved 88.70% accuracy on lynx in complex habitats.

Demonstrated effectiveness across different species and environments.

Utilized existing datasets for automatic label generation.

Abstract

The bilateral asymmetry of flanks of animals with visual body marks that uniquely identify an individual, complicates tasks like population estimations. Automatically generated additional information on the visible side of the animal would improve the accuracy for individual identification. In this study we used transfer learning on popular CNN image classification architectures to train a flank predictor that predicts the visible flank of quadruped mammalian species in images. We automatically derived the data labels from existing datasets originally labeled for animal pose estimation. We trained the models in two phases with different degrees of retraining. The developed models were evaluated in different scenarios of different unknown quadruped species in known and unknown environments. As a real-world scenario, we used a dataset of manually labeled Eurasian lynx (Lynx lynx) from camera traps in the Bavarian Forest National Park to evaluate the model. The best model, trained on an EfficientNetV2 backbone, achieved an accuracy of 88.70 % for the unknown species lynx in a complex habitat.