Recently, there has been a growing interest in wearable sensors which provides new research perspectives
for 360° video analysis. However, the lack of 360° datasets in literature hinders the research in this
field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° 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° environmental setup, which will facilitate the egocentric 360° video understanding.
We provide experimental results and comprehensive analysis of variants of the two-stream network for 360
egocentric activity recognition.