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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Barekatain, Mohammadamin, Marti, Miquel, Hsueh-Fu Shih, Murray, Samuel, Nakayama, Kotaro, Matsuo, Yutaka, Prendinger, Helmut
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Conferences
Data collection
Mobile communication
Surveillance
Training
Video sequences
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2160-7516
DOI:10.1109/CVPRW.2017.267