Dynamic Bayesian networks for visual recognition of dynamic gestures

被引：0

作者：

Avilés-Arriaga, HH ^{[1
]}

Sucar, LE ^{[1
]}

机构：

[1] Tec Monterrey, Cuernavaca 82589, Morelos, Mexico

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2002年 / 12卷 / 3-4期

关键词：

dynamic Bayesian networks; hidden Markov models; gesture recognition;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dynamic Bayesian networks are a powerful representation to describe processes that vary over time inside a stochastic framework. This paper describes an online visual recognition system to recognize a set of five dynamic gestures executed with the user's right hand using dynamic Bayesian networks for recognition. Gestures are oriented to command mobile robots. The system employs a radial scan segmentation algorithm combined with a statistical-based skin detection method to find the candidate face of the user and to track his right-hand. It uses four simple features to describe the user's right-hand movement. Our system is able to recognize these five gestures in real-time with an average recognition rate of 84.01%, better result than using hidden Markov models for recognition.

引用

页码：243 / 250

页数：8

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