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README.md

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@@ -25,7 +25,7 @@ manual effort associated with generating reliable behavior-dense annotated datas
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- [x] The workflow allows collecting dense behavior data over large time and spatial scales
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- [x] Enables researchers to quickly annotate animals and behaviors of interest to generate dense behavior and annotation datasets
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- [x] The underlying light-weight network architecture and implementation can be employed for real-time animal detection and behavior classification in the wild
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- [x] The workflow is built around videos from consumer-grade single cameras enabling easy adoption for reserach and conservation in the field
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- [x] The workflow is built around videos from consumer-grade single cameras enabling easy adoption for research and conservation in the field
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We demonstrate the benefits of using this workflow on aerial video footage of zebras recorded at Mpala in Kenya. The [full annotated dataset](https://keeper.mpdl.mpg.de/d/a9822e000aff4b5391e1/) is freely available for the research community.
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@@ -51,7 +51,7 @@ A [Preprint outlining the framework](https://doi.org/10.1101/2023.07.31.551177)
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You can install via pip:
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```bash
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python3 -m pip install --upgrade "git+https://github.com/robot-perception-group/smarter-labelme@master"
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python3 -m pip install --upgrade "git+https://github.com/robot-perception-group/Animal-Behaviour-Inference-Framework@master"
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```
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### Hint on Pytorch
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eprint = {https://www.biorxiv.org/content/early/2023/08/02/2023.07.31.551177.full.pdf},
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journal = {bioRxiv}
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}
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@InProceedings{10.1007/978-3-031-44981-9_12,
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author="Price, Eric and Ahmad, Aamir",
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editor="Lee, Soon-Geul et al.",
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title="Accelerated Video Annotation Driven by Deep Detector and Tracker",
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booktitle="Intelligent Autonomous Systems 18",
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year="2024",
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publisher="Springer Nature Switzerland",
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address="Cham",
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pages="141--153",
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doi = {10.1007/978-3-031-44981-9_12},
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isbn="978-3-031-44981-9"
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}
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```
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```
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