BON: An extended public domain dataset for human activity recognition

TL;DR

This paper introduces BON, a large, publicly available dataset of egocentric office activity videos captured across multiple locations, aiming to advance human activity recognition research in office environments.

Contribution

The paper provides a comprehensive, annotated dataset for egocentric office activities, filling a gap in available data for deep learning-based activity recognition in such settings.

Findings

BON dataset contains 2,639 segments across 18 activities.

Baseline activity recognition results are provided for future comparison.

Dataset covers diverse office activities and locations.

Abstract

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.