We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
- Sample (one 4K video and labels): sample
We offer the dataset in two different formats:
In addition, we provide trained models in Caffe: models
The creation of this dataset was supported by Prendinger Lab at the National Institute of Informatics, Tokyo, Japan.
