摘要
针对动态手势在时间尺度上的多变性和复杂性,提出了一种动态手势识别框架.该框架利用时间序列上提取的手势轮廓构造动态手势轮廓图像,获得不同动态手势在不同时间尺度下其轮廓图像的均值图像和方差图像,并将这些图像用于构成动态手势轮廓模型库,在此模型库基础上,利用相关信息方法和改进的动态时间规整方法完成动态手势的识别.实验结果表明,文中提出的动态手势轮廓模型对不同时间尺度的动态手势具有较强的鲁棒性,改进的动态时间规整方法较传统方法具有更高的识别率.
Proposed in this paper is a new dynamic gesture recognition framework to solve time-scale variability and complexity of dynamic gestures. This framework constitutes a dynamic gesture contour image by using the gesture contour in time series, and calculates the mean and variance images of gray-scale images in different time scales. Furthermore, these mean and variance images are organized into a dynamic gesture contour model library. On this basis, dynamic gestures are recognized by using correlated information method and improved dynamic time warping method. Experimental results show that the proposed dynamic gesture contour model is of strong robustness to the dynamic gestures in various time scales, and that the improved dynamic time warping method helps obtain recognition rate higher than that of the traditional method.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第1期140-146,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
上海科学与技术委员会攻关研究项目(11511503300)~~
关键词
手势识别
时间序列
轮廓模型
相关信息法
动态时间规整
gesture recognition
time series
contour model
correlated information method
dynamic time warping
