The Breakfast Actions Dataset

A comprehensive dataset of breakfast preparation activities performed by 52 different individuals in 18 different kitchens, designed to reflect real-world recognition scenarios.

This dataset comprises 10 actions related to breakfast preparation. The dataset is to-date one of the largest fully annotated datasets available, designed to reflect real-world conditions for monitoring and analysis of daily activities.

Dataset specifications: ~77 hours of video (>4 million frames), 320×240 pixels resolution (down-sampled), 15 fps frame rate, 3-5 uncalibrated cameras per location.

Downloads

Videos

Main file — 3.6 GB

License: CC BY 4.0

Pre-computed Features

dense_traj_all_s1.tar.gz — ~37GB
dense_traj_all_s2.tar.gz — ~57GB
dense_traj_all_s3.tar.gz — ~42GB
dense_traj_all_s4.tar.gz — ~75GB
bf_kinetics_feat.tar.gz — 27.7 GB I3D features (RGB and flow)

Segmentation Data

Code and Documentation

HTK-based model by Kuehne et al. (2016) — Available on GitHub
MATLAB demo — Previous demo bundle for action recognition

Note: Large files are hosted externally on Dropbox or other services. Please contact the lab for access to external files.

Action Classes (10)

Current Benchmarks

Fully Supervised Learning

Activity Classification: Classify full videos according to the 10 activity classes
Temporal Segmentation: Detect and classify the 48 action units within the videos

Weakly Supervised Learning

Temporal Alignment: Detect actions given transcripts at test time
Temporal Segmentation: Detect and classify 48 action units without transcripts

Train/Test Splits

Citation

Primary paper:
H. Kuehne, A. B. Arslan and T. Serre. The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities. CVPR, 2014.

Follow-up work:
H. Kuehne, J. Gall and T. Serre. An end-to-end generative framework for video segmentation and recognition. WACV, 2016.

The Breakfast Actions Dataset Official