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

BON: An extended public domain dataset for human activity recognition