Publication
Understanding behavior requires datasets that capture humans while carrying out complex tasks. The kitchen is an excellent environment for assessing human motor and cognitive function, as many complex actions are naturally exhibited in kitchens from chopping to cleaning. Here, we introduce the EPFL-Smart-Kitchen-30 dataset, collected in a noninvasive motion capture platform inside a kitchen environment. Nine static RGB-D cameras, inertial measurement units (IMUs) and one head-mounted HoloLens~2 headset were used to capture 3D hand, body, and eye movements. The EPFL-Smart-Kitchen-30 dataset is a multi-view action dataset with synchronized exocentric, egocentric, depth, IMUs, eye gaze, body and hand kinematics spanning 29.7 hours of 16 subjects cooking four different recipes. Action sequences were densely annotated with 33.78 action segments per minute. Leveraging this multi-modal dataset, we propose four benchmarks to advance behavior understanding and modeling through
⚠️ videos and other collected data can be found at https://zenodo.org/records/15535461
Public_release_pose.zipPublic_release_pose
├── README.md
├── train
| ├── YH2002 (participant)
| | ├── 2023_12_04_10_15_23 (session)
| | | ├── annotations
| | | | ├── action_annotations.xlsx
| | | | └── activity_annotations.json
| | | ├── pose_3d
| | | | ├── pose3d_mano