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 base- line activity recognition results
with a number of existing methods. The dataset is provided along with its
annotation and the extracted features.
G. Abebe, A. Catala, A. Cavallaro, “A first-person vision dataset
of office activities”, Proc. of Int.
Workshop on Multimodal Pattern Recognition of Social Signals in Human Computer
Interaction, Beijing, China, August 20, 2018
A few sample videos [data]
Continuous version of the video sequences (12 subjects) [1/4] [2/4] [3/4]
[4/4]
The annotation [data]
Extracted features used in the paper [data]
Segmented video sequences (12 subjects) [1/4] [2/4] [3/4]
[4/4]