![]() We evaluate our pipeline on four new datasets that we release with this paper as a benchmark at. To tackle the generic multi-animal pose-tracking scenario, we designed a practical, almost entirely data-driven solution that breaks down the larger goal into the smaller subtasks of: keypoint estimation, animal assembly (spatially grouping keypoints into individuals), local (temporal) tracking and global ‘tracklet’ stitching (Extended Data Fig. Multi-animal pose estimation can be cast as a data assignment problem in the spatial and temporal domains. Graphical user interfaces (GUIs) for keypoint annotation, refinement and semiautomatic trajectory verification. Unsupervised animal ID tracking: we can predict the identity of animals and reidentify them this is particularly useful to link animals across time when temporally based tracking fails (due to intermittent occlusions). Multi-task architecture that predicts multiple conditional random fields and therefore can predict keypoints, limbs, as well as animal identity.Ī data-driven method for animal assembly that finds the optimal skeleton without user input, and that is state of the art (compared to top-models from COCO, a standard computer vision benchmark).Ī module that casts tracking as a network flow optimization problem, which aims to find globally optimal solutions. Our contributions are as follows:įour datasets of varying difficulty for benchmarking multi-animal pose estimation networks. Specifically, we expanded DeepLabCut 19, 20, 21, an open-source toolbox for animal pose estimation. Here, we developed top-performing network architectures, a data-driven assembly method, engineered tailored tracking methods and compared the current state-of-the-art networks on COCO (common objects in context) 18 on four animal datasets. Building on human pose estimation research, some packages for multi-animal pose estimation have emerged 15, 16, 17. Tracking animals between frames can be difficult because of appearance similarity, nonstationary behaviors and possible occlusions. To associate detected keypoints to particular individuals (assembly) several solutions have been proposed, such as part affinity fields (PAFs) 9, associative embeddings 10, 11, transformers 12 and other mechanisms 13, 14. ![]() To make pose estimation robust to interacting and occluded animals, one should annotate frames with closely interacting animals. In general, the process requires three steps: pose estimation (that is, keypoint localization), assembly (that is, the task of grouping keypoints into distinct animals) and tracking. Multi-animal pose estimation raises several challenges that can leverage advances in machine vision research, and yet others that need new solutions. Many experiments in biology-from parenting mice to fish schooling-require measuring interactions among multiple individuals. ![]() For the computational analysis of fine-grained behavior, pose estimation is often a crucial step and deep-learning based tools have quickly affected neuroscience, ethology and medicine 5, 6, 7, 8. Computer vision is a crucial tool for identifying, counting, as well as annotating animal behavior 2, 3, 4. ![]() Advances in sensor and transmitter technology, data mining and computational analysis herald a golden age of animal tracking across the globe 1. ![]()
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