深度图像手势分割及HOG-SVM手势识别方法研究

摘要

针对深度图像静态手势识别问题，提出一种基于深度图像手势分割及 HOG-SVM手势识别方法。该方法的具体做法包含以下四个步骤：第一步，对深度图像进行手势分割，对随机方向的手臂图像通过椭圆拟合算法计算其倾斜角度，并将其校正至垂直方向；第二步，对手臂图像进行距离变换，通过分析距离变换返回的距离矩阵精确定位手掌心、手腕及手臂在图像中的坐标；第三步，计算、优化手势图像的 HOG 特征；第四步，实时采集大量训练样本并获取其训练矩阵，对训练矩阵进行处理找到最优的 SVM参数，使响应曲线的可区分度达到最佳以提高手势识别率。实验证明，所设计的系统在保证实时性、鲁棒性的同时也获得了很高的识别率。%Aiming at the static gesture recognition of depth image,this paper proposes a depth image-based gesture segmentation and HOG-SVMgesture recognition method.The specific approach of the method contains following four steps.First,we carry out hand gesture segmenta-tion on depth image and calculate tilt angle of the arm image in stochastic direction by ellipse fitting algorithm,and then regulate it to vertical direction.Secondly,we make distance transform on arm image,and precisely locate the coordinates of palm,wrist and arm in the image by ana-lysing the distance matrix returned from distance transform.Thirdly,we calculate and optimise the HOG features of gesture image.Finally,we collect in real time a large number of training samples and obtain their training matrix,process the training matrix to find the optimal SVMpa-rameters,thus enabling the distinguishable degree of the response curve to reach the best so as to improve gesture recognition rate.It is proved by the experiment that the designed system in the paper achieves quite high recognition rate while ensuring real-time performance and robust-ness.