计算机应用与软件2017，Vol.34Issue(2)：198-202,213,6.DOI:10.3969/j.issn.1000-386x.2017.02.035
基于Leap Motion传感器的自适应动态手势识别
ADAPTIVE DYNAMIC GESTURE RECOGNITION BASED ON LEAP MOTION
摘要
Abstract
Gesture recognition based on vision is a key technology to realize new human-computer interaction.To the problem of gestures adaptability and recognition rate,a new method of adaptive dynamic gesture recognition based on the Leap Motion is presented on the basis of improving Hidden Markov Model (HMM).Firstly,by using the method of geometric features to recognize the static hand posture,the start and end point of dynamic gesture trajectory could be confirmed.After extracting and classifying gesture trace features by angle,the revised revaluation method is introduced to calculate model parameters.Finally,with recognizing the undefined gesture,the method of automatic learning and updating HMM is presented to improve the efficiency of the gesture recognition,and the dynamic gesture recognition of 26 lowercase letters is realized in the end.Experimental results show this method has a good adaptability and accuracy performance in dynamic gesture recognition.关键词
隐马尔可夫模型/Leap Motion/动态手势识别/特征提取Key words
Hidden Markov model/Leap Motion/Dynamic gesture recognition/Feature extraction分类
信息技术与安全科学引用本文复制引用
刘权,陈一民,高明柯,李启明,黄晨..基于Leap Motion传感器的自适应动态手势识别[J].计算机应用与软件,2017,34(2):198-202,213,6.基金项目
上海市国际科技合作基金项目(12510708400) （12510708400）
上海市自然科学基金项目(14ZR1419700) （14ZR1419700）
2015上海大学电影学院高峰学科项目(N13A30315W23). （N13A30315W23）
