Indoor Action Dataset

We are releasing the Indoor video dataset for action recognition presented in [1]. This dataset includes recordings of several common scenes for indoor daily life activities. It was built recording samples of an average of 10-minute videos at 25 fps with people carrying out indoor actions. Afterwards, the videos were manually curated and sampled into 3-to-5-second labeled clips. In total, five different subjects collaborated in the recordings, in a variety of indoor scenarios such as bedrooms, kitchens, or living rooms.

The dataset includes samples of ten different classes of activities such as cleaning, eating, sitting down, standing up, blowing nose, walking, watching tv as well as classes that represent potentially risky situations such as falling down or lying on the floor. Additionally, we included a no-action class with indoor spaces where no activity is carried out or no one is on the scene.

In the following table we provide information about how many clips were recorded by each subjects.

In addition, the Table below shows the number of samples retrieved per class or daily life activity.

git clone https://github.com/DaniDeniz/IndoorActionDataset.git
cd IndoorActionDataset
chmod +x setup_video_data.sh && ./setup_video_data.sh

Full lenght videos were manually cropped into individual clips, and then this clips were assigned to a set (train, validation or test). Bear in mind that all clips for each video are assigned to the same set, avoiding having train, validation and test samples with similar propierties. We are providing the clips with HD (720p) resolution.

The name of each clip is as follows:

20210114 _ 150036 − 151348 [ 150942.072000 , 150950.572000 ] ( L i v i n g R o o m 1 − S 0 )

Which represents:

d a t e ( Y Y Y Y M M D D ) _ s t a r T i m e V i d e o ( H H M M S S ) − f i n i s h T i m e V i d e o ( H H M M S S )

[ s t a r t T i m e C l i p ( H H M M S S . f ) , f i n i s h T i m e C l i p ( H H M M S S . f ) ] ( S c e n a r i o I D − S u b j e c t I D )

Note that with Video we refer to the original video of continuous recording, and with Clip we refer to each of the samples extracted from each video. Therefore, every clip recorded in the same scenario by the same subject at the same Video time means that these clips were retrieved from the same video. The start and finish Clip time points out when the action occurred in the recorded Video.

[1] D. Deniz, E. Ros, E. M. Ortigosa, F. Barranco. "Optimized edge-cloud system for activity monitoring using knowledge distillation" in Electronics, 13 (23), 2024.

@article{deniz2024optimized,
  title={Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation},
  author={Deniz, Daniel and Ros, Eduardo and Ortigosa, Eva M and Barranco, Francisco},
  journal={Electronics},
  volume={13},
  number={23},
  pages={4786},
  year={2024},
  publisher={MDPI}
}