Desktop Action Recognition From First-Person Point-of-View
该研究发布了首个第一人称视角桌面动作识别基准数据集，包含六名受试者的日常桌面活动视频，并基于手部时空信息提出特征表示方法，在五类动作上取得良好识别效果。
Desktop action recognition from first-person view (egocentric) video is an important task due to its omnipresence in our daily life, and the ideal first-person viewing perspective for observing hand-object interactions. However, no previous research efforts have been dedicated on the benchmark of the task. In this paper, we first release a dataset of daily desktop actions recorded with a wearable camera and publish it as a benchmark for desktop action recognition. Regular desktop activities of six participants were recorded in egocentric video with a wide-angle head-mounted camera. In particular, we focus on five common desktop actions in which hands are involved. We provide original video data, action annotations at frame-level, and hand masks at pixel-level. We also propose a feature representation for the characterization of different desktop actions based on the spatial and temporal information of hands. In experiments, we illustrate the statistical information about the dataset, and evaluate the action recognition performance of different features as a baseline. The proposed method achieves promising performance for five action classes.