  Recently, there has been a growing interest in wearable sensors which
provides new research perspectives for 360 {\deg} video analysis. However, the
lack of 360 {\deg} datasets in literature hinders the research in this field.
To bridge this gap, in this paper we propose a novel Egocentric (first-person)
360{\deg} Kinetic human activity video dataset (EgoK360). The EgoK360 dataset
contains annotations of human activity with different sub-actions, e.g.,
activity Ping-Pong with four sub-actions which are pickup-ball, hit,
bounce-ball and serve. To the best of our knowledge, EgoK360 is the first
dataset in the domain of first-person activity recognition with a 360{\deg}
environmental setup, which will facilitate the egocentric 360 {\deg} video
understanding. We provide experimental results and comprehensive analysis of
variants of the two-stream network for 360 egocentric activity recognition. The
EgoK360 dataset can be downloaded from https://egok360.github.io/.
