# BON: An extended public domain dataset for human activity recognition

arXiv: https://arxiv.org/abs/2209.05077 · HTML: https://mlsanity.com/paper/2209.05077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition](https://mlsanity.com/paper/1807.11794/) (2018)
- [Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks](https://mlsanity.com/paper/1705.06709/) (2017)
- [Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network](https://mlsanity.com/paper/1912.01811/) (2019)
- [Privacy-Aware Activity Classification from First Person Office Videos](https://mlsanity.com/paper/2006.06246/) (2020)

## Built on by (most cited first, 2 of 2)

- [Egocentric Gaze Estimation via Neck-Mounted Camera](https://mlsanity.com/paper/2602.11669/) (2026)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)

## Abstract (arXiv)

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.