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.
Recently, Barekatain et al. REF proposed a new video dataset for aerial view concurrent human action detection.
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science