为实现基于Kinect传感器的实时手势识别,并在保证识别精度的情况下缩短识别时间,提出一种基于卡尔曼滤波的手势图像提取方法,并研究基于该分割方法的三种特征的手势识别模型。通过Kinect获取图像和骨骼信息,基于卡尔曼滤波提取手势区域。为验证分割的高效性,采集10类手势的28000张样本,提取两种局部二值模式特征和一种方向梯度直方图(HOG)特征,用支持向量机(SVM)机器学习方法进行分类识别。实验表明,HOG+SVM的手势识别模型的识别精度可达97.09%,识别帧率为31 frame/s。在基于Kinect的应用中,基于该分割方法和HOG特征提取的SVM识别模型能够满足实时性的要求。

In order to realize real-time gesture recognition based on Kinect and to reduce the recognition time while ensuring the recognition accuracy, we propose a method of gesture image extraction based on Kalman filter, and study a gesture recognition model based on three characteristics. We get depth images and skeleton information via Kinect, and then extract hand regions based on Kalman filter. In order to verify the efficiency of gesture segmentation, we collect 28000 samples of 10 types of gestures, extract two local binary pattern features and histogram of oriented gradient (HOG) feature, and classify the samples by support vector machine (SVM). The experimental results show that the gesture recognition model based on HOG+SVM has the recognition accuracy of 97.09% and the recognition rate of 31 frame/s. In application based on Kinect, HOG+SVM recognition model based on the proposed segmentation method can meet the real-time requirement.