About this Dataset
The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals.
Typically, datasets for animal-specific vision tasks are created using open-source video material. This might be effective for an initial start, but these methods are not deployment ready for the behavior community. Therefore, we designed a semi-automated method for biologists to create well-curated datasets at a large scale for the ML and Vision community.
3D-POP is the first dataset with 3D ground truth for multi-animal, multi-view tracking problems.
Highlight: The dataset is captured with the intention of using it for various vision problems and with different levels of complexity (no of cameras, no of individuals)
Video explanation: Link to YouTube video
Video teaser: Link to YouTube video
Dataset Features:
Marker-based videos:
- 6 hours+ of annotations of 18 individuals (groups of 1, 2, 5, 10).
- Bounding box
- Trajectories (2D and 3D)
- Posture (2D and 3D) with 9 key points
- Identities
- Total of 57 sequences (4K) with 4 views.
- Dataset customization* (Users can modify the dataset and add key points to the dataset)
Markerless:
- 1Hr+ videos of 18 individuals in groups of 1, 2, 5, 11. The birds have no markers. This data is provided as test cases and unsupervised approaches.
