Egok360 A 360 Egocentric Kinetic Human Activity Video Dataset

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. The EgoK360 dataset can be downloaded from https://egok360.github.io/.

近年来，可穿戴传感器受到学界的广泛关注，为360°视频分析领域带来了全新的研究视角。然而，现有学术文献中360°数据集的匮乏制约了该领域的研究进展。为填补这一研究空白，本文提出了一款全新的自我中心（first-person，第一人称视角）360°动态人体活动视频数据集（EgoK360）。EgoK360数据集包含带有不同子动作的人体活动标注，例如乒乓球活动包含拾球、挥拍、弹球和发球四个子动作。据我们所知，EgoK360是首个面向360°环境下的第一人称人体活动识别领域的数据集，将助力自我中心视角360°视频的理解研究。本文还提供了针对360°自我中心视角活动识别的双流网络（two-stream network）各类变体的实验结果与全面分析。研究人员可通过https://egok360.github.io/下载该数据集。