Device-free people counting and localization

  • Authors:
  • Chenren Xu

  • Affiliations:
  • Rutgers University, North Brunswick , NJ, USA

  • Venue:
  • Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
  • Year:
  • 2013

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Abstract

Device-free passive (DfP) localization has been proposed as an emerging technique for localizing people, without requiring them to carry any devices. Potential applications include elder-care, security enforcement, building occupancy statistics, etc. We first present PC-DfP, an accurate and efficient RF-based device-free localization solution. PC-DfP adopts a stochastic fingerprinting approach to mitigate the error caused by the multipath and meanwhile minimize the system calibration overhead. Second, we present SCPL, a RF-based device-free people counting and localization technique. SCPL takes the calibration data collected with one person and the map information to accurately count people sequentially and localize them in parallel. Finally we present Crowd++, an unsupervised speaker counting technique through audio inference with smartphones to estimate the number of people in social hotspot places.