@inproceedings{5121c4a8625b4e2895575180ea365ade,
title = "WIP abstract: Deep intelligent network for device-free people tracking",
abstract = "Recent radio frequency (RF) sensing techniques use a network of RF sensors to detect and locate people that do not carry any devices and can operate in non line-of-sight environments. Model-based device-free RF sensing systems use statistical models to quantify human presence and motion based on the received RF signal measurements. However, such methods often require the fine tuning of multiple model-dependent parameters in order to achieve sub meter accuracy. In this work, we propose to use deep neural networks together with visual tracking systems to effectively generate training data so as to learn a general model. Our method can automatically produce human motion and occupancy images from RF sensor network measurements without the need for manual RF model parameter tuning.",
keywords = "Deep Neural Networks, Detection, RF Sensor Network, Tracking",
author = "Yang Zhao and Chang, \{Ming Ching\} and Peter Tu",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, part of the 2019 CPS-IoT Week ; Conference date: 16-04-2019 Through 18-04-2019",
year = "2019",
month = apr,
day = "16",
doi = "10.1145/3302509.3313312",
language = "英语",
series = "ICCPS 2019 - Proceedings of the 2019 ACM/IEEE International Conference on Cyber-Physical Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "302--303",
editor = "Ramachandran, \{Gowri Sankar\} and Jorge Ortiz",
booktitle = "ICCPS 2019 - Proceedings of the 2019 ACM/IEEE International Conference on Cyber-Physical Systems",
}