Personalized Gestures Through Motion Transfer: Protecting Privacy in Pervasive Surveillance

Research output: Contribution to journalArticleScientificpeer-review

Abstract

With the growing ubiquitousness of pervasive sensing and toward ambient intelligence, pervasive surveillance becomes a very real privacy threat, where private gesture interaction is likely to be observed and automatically interpreted by other (even benign) pervasive intelligence tools. We propose motion transfer, the example-guided modification of motion to translate from default motion and gesture interaction alphabets to personal ones. Apart from privacy, incentive to use personalized gesture interaction alphabets include convenience as well as physical handicaps (i.e., inability to conduct certain movements). We demonstrate the concept using motion transfer in RGB-video data. We further show that the approach is feasible also for point-cloud-based gesture recognition methods. In particular, we implemented an end-to-end model for human motion transfer with 3D (<italic>x</italic>-<italic>y</italic>-time) or 4D (<italic>x</italic>-<italic>y</italic>-<italic>z</italic>-time) point-cloud datasets. Point-cloud-based motion transfer is a privacy protecting way of customizing gestures to control devices, hence lowering the risk of disclosing the nature of interaction to surrounding pervasive surveillance installations.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Pervasive Computing
Early online date13 Oct 2022
DOIs
Publication statusE-pub ahead of print - 13 Oct 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Decoding
  • Drones
  • Privacy
  • Sensors
  • Shape
  • Three-dimensional displays
  • Training

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