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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:

• 1280x720 frames + labels: train set, test set
• 4K videos + labels: train set, test set

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.