Beamsteering for Training-free Counting of Multiple Humans Performing Distinct Activities

Sameera Palipana, Nicolas Malm, Stephan Sigg

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
76 Downloads (Pure)

Abstract

Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits antenna installations, for instance, found in upcoming 5G technology, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-sweeping over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4 m2 area with an accuracy that significantly exceeds the related work). 2) We demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.

Original languageEnglish
Title of host publication18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020
PublisherIEEE
Number of pages10
ISBN (Electronic)9781728146577
DOIs
Publication statusPublished - Mar 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Pervasive Computing and Communications - Austin, United States
Duration: 23 Mar 202027 Mar 2020
Conference number: 18

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications
Abbreviated titlePerCom
Country/TerritoryUnited States
CityAustin
Period23/03/202027/03/2020

Keywords

  • Beamsteering
  • Multi-subject recognition
  • Radio sensing
  • Training-free crowd counting

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