# A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition

arXiv: https://arxiv.org/abs/2205.11962 · HTML: https://mlsanity.com/paper/2205.11962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [ArtTrack: Articulated Multi-person Tracking in the Wild](https://mlsanity.com/paper/1612.01465/) (2016)
- [Can WiFi Estimate Person Pose?](https://mlsanity.com/paper/1904.00277/) (2019)
- [Parsing Occluded People by Flexible Compositions](https://mlsanity.com/paper/1412.1526/) (2014)
- [Exploiting WiFi Channel State Information for Residential Healthcare Informatics](https://mlsanity.com/paper/1712.03401/) (2017)
- [Wi-Fi CSI based Behavior Recognition: From Signals, Actions to Activities](https://mlsanity.com/paper/1712.00146/) (2017)
- [CSI-Net: Unified Human Body Characterization and Pose Recognition](https://mlsanity.com/paper/1810.03064/) (2018)

## Abstract (arXiv)

Human Activity Recognition (HAR) has recently received remarkable attention in numerous applications such as assisted living and remote monitoring. Existing solutions based on sensors and vision technologies have obtained achievements but still suffering from considerable limitations in the environmental requirement. Wireless signals like WiFi-based sensing have emerged as a new paradigm since it is convenient and not restricted in the environment. In this paper, a new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition where the synchronized video serves as the supplement for the wireless data. Moreover, a wireless-vision benchmark (WiVi) is collected for 9 class actions recognition in three different visual conditions, including the scenes without occlusion, with partial occlusion, and with full occlusion. Both machine learning methods - support vector machine (SVM) as well as deep learning methods are used for the accuracy verification of the data set. Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than 80% of activity recognition accuracy over multiple action segmentation from 1s to 3s. In particular, WiNN is the most robust method in terms of all the actions on three action segmentation compared to the others.