基于旋转不变深度比较特征的人手关节点识别

Human Hand Joint Recognition Based on Rotation Invariant Depth Comparison Features

针对基于深度图像的手部关节难以识别和不同关节难以区分的问题,提出一种基于旋转不变深度比较特征的人手关节点识别算法。首先提取旋转不变深度比较特征用于随机森林分类器的训练和像素的部位识别,再计算部位的逐点映射形心的密度实现关节点识别。实验结果表明,此算法具有较高的识别准确率和鲁棒性。

Aiming at problem that the hand joint is difficult to identify and different joint is difficult to distinguish based on depth image, an algorithm based on rotation invariant depth comparison feature is proposed. First, the rotation invariant depth compari- son feature is extracted to train random forest classifier and identify the part category of pixel. Then density of centroid of per- point mapping is calculated to achieve joint recognition. The experimental result shows that the algorithm is of high accuracy and robustness.