TY - JOUR
T1 - Analysis of composite gestures with a coherent probabilistic graphical model
AU - Corso, Jason J.
AU - Ye, Guangqi
AU - Hager, Gregory D.
PY - 2005/9
Y1 - 2005/9
N2 - 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.
AB - 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.
KW - Gesture recognition
KW - Hand postures
KW - Human computer interaction
KW - Probabilistic graphical model
KW - Vision-based interaction
UR - http://www.scopus.com/inward/record.url?scp=25844482401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=25844482401&partnerID=8YFLogxK
U2 - 10.1007/s10055-005-0157-1
DO - 10.1007/s10055-005-0157-1
M3 - Article
AN - SCOPUS:25844482401
SN - 1359-4338
VL - 8
SP - 242
EP - 252
JO - Virtual Reality
JF - Virtual Reality
IS - 4
ER -