VICs: A modular vision-based HCI framework

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

Research output: Contribution to journalArticle

Abstract

Many Vision-Based Human-Computer Interaction (VB-HCI) systems are based on the tracking of user actions. Examples include gaze-tracking, head-tracking, finger-tracking, and so forth. 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 image 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. The system operates at frame-rate on standard PCs.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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