Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data
This work addresses the problem of robust behavior tracking for neuroscience researchers, but it is incremental as it evaluates existing model architectures on new data.
The paper tackled the challenge of 3D pose estimation for freely moving rodents in laboratory settings by proposing lift-pose models that use single-view camera data, achieving reliable 3D pose inference with temporal convolutions.
The assessment of laboratory animal behavior is of central interest in modern neuroscience research. Behavior is typically studied in terms of pose changes, which are ideally captured in three dimensions. This requires triangulation over a multi-camera system which view the animal from different angles. However, this is challenging in realistic laboratory setups due to occlusions and other technical constrains. Here we propose the usage of lift-pose models that allow for robust 3D pose estimation of freely moving rodents from a single view camera view. To obtain high-quality training data for the pose-lifting, we first perform geometric calibration in a camera setup involving bottom as well as side views of the behaving animal. We then evaluate the performance of two previously proposed model architectures under given inference perspectives and conclude that reliable 3D pose inference can be obtained using temporal convolutions. With this work we would like to contribute to a more robust and diverse behavior tracking of freely moving rodents for a wide range of experiments and setups in the neuroscience community.