A first-person vision dataset of office activities

Abstract

We present a multi-subject first-person vision dataset of office activities. The dataset contains the highest number of subjects and activities compared to existing office activity datasets. Office activities include person-to-person interactions, such as chatting and handshaking, person-to-object interactions, such as using a computer or a whiteboard, as well as generic activities such as walking. The videos in the dataset present a number of challenges that, in addition to intra-class differences and inter-class similarities, include frames with illumination changes, motion blur, and lack of texture. Moreover, we present and discuss state-of-the-art features extracted from the dataset and baseline activity recognition results with a number of existing methods. The dataset is provided along with its annotation and the extracted features.