Abstract
Traditionally, gesture-based interaction in virtual environments is composed of either static, posture-based gesture primitives or temporally analyzed dynamic primitives. However, it would be ideal to incorporate both static and dynamic gestures to fully utilize the potential of gesture-based interaction. To that end, we propose a probabilistic framework that incorporates both static and dynamic gesture primitives. We call these primitives Gesture Words (GWords). Using a probabilistic graphical model (PGM), we integrate these heterogeneous GWords and a high-level language model in a coherent fashion. Composite gestures are represented as stochastic paths through the PGM. A gesture is analyzed by finding the path that maximizes the likelihood on the PGM with respect to the video sequence. To facilitate online computation, we propose a greedy algorithm for performing inference on the PGM. The parameters of the PGM can be learned via three different methods: supervised, unsupervised, and hybrid. We have implemented the PGM model for a gesture set of ten GWords with six composite gestures. The experimental results show that the PGM can accurately recognize composite gestures.
Original language | English (US) |
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Pages (from-to) | 242-252 |
Number of pages | 11 |
Journal | Virtual Reality |
Volume | 8 |
Issue number | 4 |
DOIs | |
State | Published - Sep 2005 |
Keywords
- Gesture recognition
- Hand postures
- Human computer interaction
- Probabilistic graphical model
- Vision-based interaction
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design