  Body-worn first-person vision (FPV) camera enables to extract a rich source
of information on the environment from the subject's viewpoint. However, the
research progress in wearable camera-based egocentric office activity
understanding is slow compared to other activity environments (e.g., kitchen
and outdoor ambulatory), mainly due to the lack of adequate datasets to train
more sophisticated (e.g., deep learning) models for human activity recognition
in office environments. This paper provides details of a large and publicly
available office activity dataset (BON) collected in different office settings
across three geographical locations: Barcelona (Spain), Oxford (UK) and Nairobi
(Kenya), using a chest-mounted GoPro Hero camera. The BON dataset contains
eighteen common office activities that can be categorised into person-to-person
interactions (e.g., Chat with colleagues), person-to-object (e.g., Writing on a
whiteboard), and proprioceptive (e.g., Walking). Annotation is provided for
each segment of video with 5-seconds duration. Generally, BON contains 25
subjects and 2639 total segments. In order to facilitate further research in
the sub-domain, we have also provided results that could be used as baselines
for future studies.
