基于动态森林模型的人体运动建模方法

Modeling Method of Human Motion Based on Dynamic Forest Model

现有的人体运动建模方法无法应用于一般非线性非高斯情况下的人体运动。针对上述不足,以马尔科夫模型为基础,提出一种改进的人体运动建模方法。利用自回归(AR)树表示人体运动预测的马尔科夫过程,对AR树进行扩展,引入动态森林模型(DFM),给出DFM的训练和正规化方法,实现对人体运动的准确建模。实例研究结果表明,DFM在各种场景下的性能均优于其他基准算法及隐马尔科夫模型和高斯过程动态模型,且计算效率较高。

Aiming at the disadvantages that the existing modeling methods of human motion cannot be applied at the general nonlinear and non-Gaussian cases,based on the Markoff model,an improved modeling method of human motion is proposed. The Markov process of human motion prediction is presented by using the Auto Regressive （AR） tree, and the paper proposes extensions to AR trees and introduces the Dynamic Forest Model （DFM） and describes its training, regularization and the realization of the accurate modeling of human motion. Example research results show that performance of DFM is better than other benchmark algorithms, Hidden Markov Model（HMM） and Gaussian Process Dynamical Model（GPDM） ,and computing efficiency of the proposed methods is high.