VICs: A modular HCI framework using spatiotemporal dynamics

Guangqi Ye, Jason J. Corso, Darius Burschka, Gregory D. Hager

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Many vision-based human-computer interaction systems are based on the tracking of user actions. Examples include gaze tracking, head tracking, finger tracking, etc. In this paper, we present a framework that employs no user tracking; instead, all interface components continuously observe and react to changes within a local neighborhood. More specifically, components expect a predefined sequence of visual events called visual interface cues (VICs). VICs include color, texture, motion, and geometric elements, arranged to maximize the veridicality of the resulting interface element. A component is executed when this stream of cues has been satisfied. We present a general architecture for an interface system operating under the VIC-based HCI paradigm and then focus specifically on an appearance-based system in which a hidden Markov model (HMM) is employed to learn the gesture dynamics. Our implementation of the system successfully recognizes a button push with a 96% success rate.

Original languageEnglish
Pages (from-to)13-20
Number of pages8
JournalMachine Vision and Applications
Issue number1
StatePublished - Dec 2004
Externally publishedYes


  • Gesture recognition
  • Human-computer interaction
  • Vision-based interaction


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