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Object recognition, localization and private object interaction tracking for maintenance-free IoT

对象识别、定位和私有对象交互跟踪，实现免维护物联网

基本信息

批准号：
22K17883
负责人：
エルデーイヴィクトル
金额：
$ 3万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Early-Career Scientists
财政年份：
2022
资助国家：
日本
起止时间：
2022-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K17883/
关键词：
Object recognition wireless sensing localization backscatter tags object recognition privacy interaction tracking

项目摘要

Object recognition: We prepared and submitted a full paper to the IEEE IoT Journal. We developed RadioRec, a deep learning-based system that recognizes everyday objects based on their interactions with microwave signals in a contact-less manner. RadioRec is a significant upgrade compared to our earlier workshop paper, both in terms of the learning capabilities and in terms of evaluation. RadioRec works by transmitting a microwave signal through the object using a single antenna pair. RadioRec extracts features automatically using an autoencoder, and uses them to train an object classification model. Our evaluation shows that RadioRec can detect and recognize 26 everyday objects of various materials and shapes with an accuracy of over 97%.The paper was rejected. The reviewers requested additional evaluation in a different environment, and also several clarifications in the text. We are now working on addressing the reviewers’ comments and preparing an updated submission to the same journal, as suggested by the reviewers. So far, we have performed additional experiments in another environment, with various object orientations and object positions, and have obtained promising preliminary results.Object localization: We have prepared a preliminary implementation of SpotFi (DOI: 10.1145/2785956.2787487) for the USRP software-defined radio platform to enable the simultaneous estimation of angle of arrival and time of flight on backscatter tags. We are currently working on getting the estimation to work correctly in a controlled environment (for now, without backscatter tags).

目标识别：我们准备并提交了一篇完整的论文给IEEE IoT Journal。我们开发了RadioRec，这是一个基于深度学习的系统，可以通过非接触方式识别日常物品与微波信号的相互作用。与我们之前的研讨会论文相比，RadioRec是一个重大的升级，无论是在学习能力方面还是在评估方面。RadioRec的工作原理是使用一对天线对将微波信号通过物体传输。RadioRec使用自动编码器自动提取特征，并使用它们来训练对象分类模型。我们的评估表明，RadioRec可以检测和识别26种不同材料和形状的日常物体，准确率超过97%。论文被退稿了。审稿人要求在不同的环境下进行进一步评价，并要求在案文中作出若干澄清。根据审稿人的建议，我们现在正在处理审稿人的意见，并准备向同一期刊提交更新的投稿。到目前为止，我们已经在另一个环境中进行了额外的实验，使用了不同的物体方向和物体位置，并获得了很好的初步结果。目标定位：我们已经为USRP软件定义无线电平台准备了SpotFi （DOI: 10.1145/2785956.2787487）的初步实现，以便同时估计反向散射标签的到达角度和飞行时间。我们目前正在努力使估计在受控环境中正确工作（目前，没有反向散射标签）。