Environment-independent In-baggage Object Identification Using WiFi Signals

Cong Shi, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, Yingying Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Low-cost in-baggage object identification is highly demanded in enhancing public safety and smart manufacturing. Existing approaches usually require specialized equipment and heavy deployment overhead, making them hard to scale for wide deployment. The recent WiFi-based approach is unsuitable for practical deployment as it did not address dynamic environmental impacts. In this work, we propose an environment-independent in-baggage object identification system by leveraging low-cost WiFi. We exploit the channel state information (CSI) to capture material and shape characteristics to facilitate fine-grained inbaggage object identification. A major challenge of building such a system is that CSI measurements are sensitive to real-world dynamics, such as different types of baggage, time-varying ambient noises and interferences, and different deployment environments. To tackle these problems, we develop WiFi features based on polarized directional antennas that can capture objects' material and shape characteristics. A convolutional neural network-based model is developed to constructively integrate the WiFi features and perform accurate in-baggage object identification. We also develop a material-based domain adaptation using adversarial learning to facilitate fast deployments in different environments. We conduct extensive experiments involving 14 representation objects, 4 types of bags in 3 different room environments. The results show that our system can achieve over 97% in the same environment, and our domain adaptation method can improve the object identification accuracy by 42% when the system is deployed in a new environment with little training.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-79
Number of pages9
ISBN (Electronic)9781665449359
DOIs
StatePublished - 2021
Externally publishedYes
Event18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021 - Virtual, Online, United States
Duration: Oct 4 2021Oct 7 2021

Publication series

NameProceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021

Conference

Conference18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/4/2110/7/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

Keywords

  • Object Identification
  • WiFi Sensing

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