Article Synopsis
- Estimating animal pose in computer vision is tricky due to occlusions and the high similarity between animals, making it hard to identify individual animals during interactions.
- We enhance DeepLabCut, an existing pose estimation tool, by adding features for tracking multiple animals and predicting their identities, which helps during occlusions.
- Our framework is tested on four diverse datasets that we are releasing to support future research in this area.
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Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007739 | PMC |
| http://dx.doi.org/10.1038/s41592-022-01443-0 | DOI Listing |
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