【摘要】 针对基于视觉的体态识别对环境要求较高、抗干扰性差等问题,提出了一种基于人体骨骼预定义的识别分类方法。该算法结合Kinect多尺度深度信息和梯度信息检测人体;基于随机森林采用正负样本互限思想识别人体各个部分,根据各部分距离构建人体姿态向量,识别骨架;再根据体态类别,构建最优分类超平面、核函数,采用改进的支持向量机进行体态分类。实验结果表明,所提算法的分类识别准确率可达94.3%,具有实时性好,抗干扰性强,鲁棒性较好等特点。更多还原

【Abstract】 In view of the problems that posture recognition based on vision requires a lot on environment and has low antiinterference capacity, a posture recognition method based on predefined bone was proposed. The algorithm detected human body by combining Kinect multi-scale depth and gradient information. And it recognized every part of body based on random forest which used positive and negative samples, built the body posture vector. According to the posture category, optimal separating hyperplane and kernel function were built by using improved support vector machine to classify postures. The experimental results show that the recognition rate of this scheme is 94. 3%, and it has good real-time performance, strong anti-interference, good robustness, etc.更多还原