摘要
传统的动态手势交互技术如隐马尔科夫模型、神经网络和统计分类器等都需要大量的训练样本,建模过程中需要领域专家的干预、对普通用户来说使用起来较为困难,并且它们针对的是特定的手势集合,很难对其进行扩展.本文通过WOZ实验,分析了用户的行为特征并给出了基于手势的数字电视交互任务模型;提出了3D动态手势状态转移模型,解决了Midas Touch问题;提出了一种基于小样本学习的动态手势识别方法,解决了传统手势识别方法的缺点;构建了个性化手势设计平台,满足了用户的个性化定制需求;实验评估结果验证了本文方法的有效性.
There are some unresolved issues left behind for many traditional dynamic gesture recognition methods ,such as Hidden Markov Model(HMM) ,Neural Network(NN) ,and statistical classifiers .For example ,they require a large number of train-ing examples and the involvement of expert users in the training process .Moreover ,they are used for some specific gesture sets which are difficult to be extended .In this paper ,we first build a task model and a state transition model for vision-based dynamic gestures .Then we propose a method for 3D dynamic gesture recognition based on small sample learning .Next we design a toolkit for development of user-defined gestures .Finally ,we develop a gesture-based interactive television prototype .Experimental results verify the validity of our method .
出处
《电子学报》
EI
CAS
CSCD
北大核心
2013年第11期2230-2236,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61202344
No.61073132)
高校基本科研业务费中山大学青年教师培育(No.1209119-17000-3161120)
中山大学958工程"全媒体时代的新闻传播创新基地"(No.90027-3284200)
关键词
人机交互
视觉手势
小样本学习
个性化交互
human-computer interaction
vision-based gestures
small sample learning
personalized interaction