Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

Mohammadamin Barekatain, Miquel Marti, Hsueh-Fu Shih, Samuel Murray, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 28-35

Abstract


Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Barekatain_2017_CVPR_Workshops,
author = {Barekatain, Mohammadamin and Marti, Miquel and Shih, Hsueh-Fu and Murray, Samuel and Nakayama, Kotaro and Matsuo, Yutaka and Prendinger, Helmut},
title = {Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}