基于二级匹配策略的实时动态手语识别

摘要

动态手语可以利用其轨迹与关键手型加以描述.大量的统计实验数据表明,大多数的常用手语通过轨迹曲线的匹配即可实现识别,因此,提出一种针对动态手语的分级匹配识别算法.首先利用体感设备获取手势轨迹,并根据轨迹的点密度分布设计了一种关键帧检测算法以提取手势的关键手型,结合轨迹的曲线特征,实现对动态手语的精确描述.然后利用优化的动态时间规整(DTW)算法完成对手语的一级匹配,即轨迹匹配.若此时可以得到识别结果,那么识别过程可以结束,否则进入二级匹配,即针对关键手型再做匹配识别,从而得到最终的识别结果.实验证明,所提算法不仅实时性好,识别的准确率也较高.%Dynamic sign language can be described by its trajectory and the key hand-action.However,a large number of statistical data show that most of the commonly used sign languages can be recognized by its trajectory curve.Therefore,a hierarchical matching recognition strategy for dynamic sign language was proposed in this paper.First,the gesture trajectory can be obtained by the somatosensory equipment like Kinect.According to its point density,an algorithm of key frame detection is designed and is used to extract the key gestures.Thus,we can achieve a precise description of dynamic sign language through trajectory curve and key frames.Then the dynamic time warping(DTW) algorithm is optimized and used to do the first-level matching,i.e.trajectory matching.If the recognition results can be get currently,the recognition process can be finished,otherwise the process should go into the second-level,i.e.key frame matching,and then get the final recognition results.Experiments show that this algorithm not only has better real-time performance,but also has higher recognition accuracy.