Real-time American sign language recognition using desk and wearable computer based video

被引：686

作者：

Starner, T ^{[1
]}

Weaver, J ^{[1
]}

Pentland, A ^{[1
]}

机构：

[1] MIT, Media Lab, Cambridge, MA 02139 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1998年 / 20卷 / 12期

关键词：

gesture recognition; hidden Markov models; wearable computers; sign language; motion and pattern analysis;

D O I：

10.1109/34.735811

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.

引用

页码：1371 / 1375

页数：5