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Subject: Computer Science, Systems and Electrical Engineering Study Type: observational

1 to 4 of 4 Results

Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation Jun 13, 2024 Chimento, Michael; Chan, Hoi Hang; Aplin, Lucy; Kano, Fumihiro, 2024, "Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation", https://doi.org/10.17617/3.ZQMOJ3, Edmond, V1 This dataset includes sample data to reproduce the 3D-SOCS pipeline, as well as an annotated dataset and trained model weights for object detection and 2D keypoint estimation for Great tits and Blue tits. For more details, code and implementation of the pipeline, we refer to: htt...
Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild Jan 3, 2024 Waldmann, Urs; Chan, Hoi Hang Alex; Naik, Hemal; Nagy, Mate; Couzin, Iain D.; Deussen, Oliver; Goldluecke, Bastian; Kano, Fumihiro, 2024, "Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild", https://doi.org/10.17617/3.ENDMTI, Edmond, V1 Wild-MuPPET is a multi-view posture dataset of foraging pigeons outdoors, with 500 frames of 3D-ground truth and 2000 frames of 2D-ground truth of 9 keypoints. Paper: https://doi.org/10.48550/arXiv.2308.15316 Git repository: https://github.com/alexhang212/3D-MuPPET
covid_twitter_data Jun 5, 2023 Jin, Zhijing, 2023, "covid_twitter_data", https://doi.org/10.17617/3.TYNBEQ, Edmond, V1
Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision Feb 8, 2023 Koger, Benjamin; Adwait Deshpande; Jeffrey T. Kerby; Jacob M. Graving; Blair R. Costelloe; Iain D. Couzin, 2023, "Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", https://doi.org/10.17617/3.EMRZGH, Edmond, V1 This is data used for the worked examples in the paper "Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", published in the Journal of Animal Ecology.

Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation

Jun 13, 2024

Chimento, Michael; Chan, Hoi Hang; Aplin, Lucy; Kano, Fumihiro, 2024, "Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation", https://doi.org/10.17617/3.ZQMOJ3, Edmond, V1

This dataset includes sample data to reproduce the 3D-SOCS pipeline, as well as an annotated dataset and trained model weights for object detection and 2D keypoint estimation for Great tits and Blue tits. For more details, code and implementation of the pipeline, we refer to: htt...

Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild

Jan 3, 2024

Waldmann, Urs; Chan, Hoi Hang Alex; Naik, Hemal; Nagy, Mate; Couzin, Iain D.; Deussen, Oliver; Goldluecke, Bastian; Kano, Fumihiro, 2024, "Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild", https://doi.org/10.17617/3.ENDMTI, Edmond, V1

Wild-MuPPET is a multi-view posture dataset of foraging pigeons outdoors, with 500 frames of 3D-ground truth and 2000 frames of 2D-ground truth of 9 keypoints. Paper: https://doi.org/10.48550/arXiv.2308.15316 Git repository: https://github.com/alexhang212/3D-MuPPET

covid_twitter_data

Jun 5, 2023

Jin, Zhijing, 2023, "covid_twitter_data", https://doi.org/10.17617/3.TYNBEQ, Edmond, V1

Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Feb 8, 2023

Koger, Benjamin; Adwait Deshpande; Jeffrey T. Kerby; Jacob M. Graving; Blair R. Costelloe; Iain D. Couzin, 2023, "Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", https://doi.org/10.17617/3.EMRZGH, Edmond, V1

This is data used for the worked examples in the paper "Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", published in the Journal of Animal Ecology.

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