@inproceedings{Waldmann2022IMuPP-58700,
  year={2022},
  doi={10.1007/978-3-031-16788-1_31},
  title={I-MuPPET : Interactive Multi-Pigeon Pose Estimation and Tracking},
  number={13485},
  isbn={978-3-031-16787-4},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Pattern Recognition : 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings},
  pages={513--528},
  editor={Andres, Björn},
  author={Waldmann, Urs and Naik, Hemal and Máté, Nagy and Kano, Fumihiro and Couzin, Iain D. and Deussen, Oliver and Goldlücke, Bastian}
}

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    <dc:contributor>Goldlücke, Bastian</dc:contributor>
    <dcterms:title>I-MuPPET : Interactive Multi-Pigeon Pose Estimation and Tracking</dcterms:title>
    <dc:creator>Deussen, Oliver</dc:creator>
    <dc:contributor>Couzin, Iain D.</dc:contributor>
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    <dcterms:abstract xml:lang="eng">Most tracking data encompasses humans, the availability of annotated tracking data for animals is limited, especially for multiple objects. To overcome this obstacle, we present I-MuPPET, a system to estimate and track 2D keypoints of multiple pigeons at interactive speed. We train a Keypoint R-CNN on single pigeons in a fully supervised manner and infer keypoints and bounding boxes of multiple pigeons with that neural network. We use a state of the art tracker to track the individual pigeons in video sequences. I-MuPPET is tested quantitatively on single pigeon motion capture data, and we achieve comparable accuracy to state of the art 2D animal pose estimation methods in terms of Root Mean Square Error (RMSE). Additionally, we test I-MuPPET to estimate and track poses of multiple pigeons in video sequences with up to four pigeons and obtain stable and accurate results with up to 17 fps. To establish a baseline for future research, we perform a detailed quantitative tracking evaluation, which yields encouraging results.</dcterms:abstract>
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    <dc:contributor>Kano, Fumihiro</dc:contributor>
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