Egocentric Household Cooking — USA (850 Hours)
Egocentric & First-Person Data
Tags and Keywords
£3,000
About
This is a SAMPLE listing representing our Egocentric (First-Person) Household Cooking dataset captured in the USA. The full collection features real-world, first-person point-of-view video of daily home kitchen operations, focusing on multi-appliance meal preparation, baking techniques, and complex recipe execution.
This listing shows a small representative sample only (see linked sample video). The full 850-hour dataset, additional hours, more participants, more kitchens, and fully custom / on-demand egocentric video collection (any activity, environment, country or capture spec) are available on request - contact us via the profile page.
Data Product Features
- Egocentric (first-person) point-of-view video of household cooking activities
- Activities: multi-appliance meal preparation, baking techniques, complex recipe execution
- Captured with GoPro Hero 11 Black (4K) head-mounted cameras
- IMU sensor data included alongside video
Distribution
- Format: MP4 video files (4K / 1080p), ZIP-packaged, plus a metadata CSV
- Data Volume: Full dataset = 850 hours. Sample shown = short representative clip (see linked sample).
- Resolution: 4K / 1080p. Frame Rate: 30 fps. Codec: H.264 / H.265.
Usage
This data product is ideal for a variety of applications:
- Application: Training egocentric/first-person action-recognition and video-language models for household/kitchen settings.
- Application: Robotics manipulation, imitation learning, and human-activity understanding in home environments.
- Application: Consumer AI research: recipe assistants, cooking-activity recognition, kitchen safety monitoring.
Coverage
- Geographic Coverage: USA (North America)
- Time Range: Rolling collection; full dataset dates available on request
- Demographics: Adults performing home cooking tasks, residential kitchen setting
License
Proprietary
AI Training Rights
Licensee is granted a non-exclusive, worldwide, and perpetual right to:
- Use the Data Product to train, fine-tune, and evaluate machine learning models, including large language models.
- Incorporate Data Product content into models and commercialize resulting model outputs.
- Create derivative works (model weights, embeddings, etc.) for any lawful purpose.
Restrictions:
- The Data Product itself may not be sold, redistributed, or shared outside of licensed usage.
- Licensee must comply with all applicable laws, including data protection and privacy regulations. All participants have provided consent for data collection and commercial licensing.
Who Can Use It
- AI/ML Teams: Training egocentric video and household-activity recognition models.
- Robotics Researchers: Manipulation, imitation learning, and human-activity datasets for home/kitchen environments.
- Data Buyers: Evaluating sample quality before licensing the full dataset or commissioning custom collection.
Data Dictionary
| Column Name | Data Type | Description | Possible Values/Notes |
|---|---|---|---|
| clip_id | String | Unique identifier for each video clip | e.g. US_COOK_0001 |
| activity | String | Primary activity performed in the clip | Meal Prep, Baking, Recipe Execution, Appliance Use |
| duration_sec | Number | Clip duration in seconds | Varies |
| location_type | String | Setting where clip was captured | Residential Kitchen |
| device | String | Capture device used | GoPro Hero 11 Black (4K), head-mounted |
| resolution | String | Video resolution | 4K, 1080p |
| fps | Number | Frames per second | 30 |
| codec | String | Video codec | H.264, H.265 |
| imu_data | Boolean | Whether IMU sensor data accompanies the clip | Yes |
Need the Full Dataset or Custom Collection?
Request access to the complete 850-hour dataset or contact us for larger-scale and custom egocentric video data collection tailored to your required activities, environments, locations and capture specifications.
Listing Stats
VIEWS
2
DELIVERY
CUSTOM, S3
LISTED
12/09/2026
UPDATED
18/09/2026
REGION
NORTH AMERICA
TRUST
5 / 5
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£3,000
Download Dataset in VIDEO Format
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