基于三维骨骼信息的动态手势识别

手势识别作为人机交互的有效手段，成为当前研究的热点话题.针对动态手势识别存在时空多变性、特征复杂性等问题，本文提出了一种基于三维骨骼信息的动态手势识别方法.动态手势具有时间上的差异性和复杂性，极大地影响了动态手势识别的准确率.因此，本文设计了一种动态手势关键帧提取算法，该算法可以提取动态手势关键部分，用于进一步的特征提取.另外，单独分类器的分类效果存在差异性，本文采用多个分类器同时对手势特征进行分类，充分利用了所提取的特征.同时，本文还提出了一种自适应融合算法，可以根据分类精度有效融合不同分类器，提高最终分类效果.最后，通过实验验证了本文提出的动态手势识别框架和方法的有效性.

Dynamic gesture recognition based on 3D skeleton information

As an effective means of human-computer interaction, gesture recognition has become a hot topic in current research. In order to solve the problems of spatio-temporal variability and feature complexity concerning dynamic gestures, we propose a dynamic gesture recognition solution based on 3D skeleton features. The accuracy of dynamic gesture recognition is greatly impaired due to the temporal differences and complexity of dynamic gestures, thus a key frame extraction algorithm is designed to extract key features of dynamic gestures for further feature extraction. To overcome the difference in classification performance between single classifiers, multiple classifiers are used to simultaneously classify and fully exploit gesture features. We also propose an adaptive fusion algorithm to effectively fuse different classifiers according to their classification performances thus improve the final classification accuracy. Finally, experiments are carried out, and results verify the effectiveness of the proposed dynamic gesture recognition approach.