摘要
提出了一种基于非监督学习的人体运动分析方法.该方法通过使用MDL准则约束下的HMM模型对连续运动序列进行分割和聚类,并实现对运动序列的自动分割和标记.该方法由两步组成,首先通过聚类将连续运动离散化,并按照最小描述长度准则在离散域得到初始解.在此基础上,返回到连续域训练MDL准则约束下的HMM模型.使用HMM模型可以进一步利用原始序列中的动态信息获得更精确的最终结果.通过对实际人体运动序列进行的实验验证了方法的有效性.
An unsupervised learning approach for analysis of human motion is proposed. In this approach, by learning a set of hidden Markov models under constrains of minimal descript length criterion, a continuous gestures sequence could be segmented and clustered, and thus the segments and labels of the original sequence are automatically extracted. The approach contains two steps. First continuous gestures are discretized and an original solution is found in discrete domain based on MDL criterion. Then coming back to continuous domain, a set of HMMs is learnt under constrains of MDL criterion, The HMMs exploit richer dynamics and thus generate better results. Experimental results by using real human gesture data demonstrate the effectiveness of the approach.
出处
《软件学报》
EI
CSCD
北大核心
2003年第2期209-214,共6页
Journal of Software
基金
国家自然科学基金~~
