计算机工程与应用2019，Vol.55Issue(15)：169-176,8.DOI:10.3778/j.issn.1002-8331.1805-0493
基于DBN-HMM的人体动作识别
Human Action Recognition Based on DBN-HMM
摘要
Abstract
The action recognition enables the machine to discriminate and understand the intention of the human action. And then it realizes efficient human-machine interaction. A new limb angle model is proposed to express the human ac-tion in 3-D space. The model has certain invariability and low computational complexity. In view of the traditional ac-tion recognition method based on Gaussian Mixture Model and Hidden Markov Model(GMM-HMM), an action recog-nition model is proposed combining the Deep Belief Network(DBN)model and Hidden Markov Model(HMM). A non-linear DBN model based on the condition restricted Boltzmann machine(CRBM)is constructed. It has strong capable to model because of its deep structure, and it combines with historical information. It is more suitable for action recognition. Experiments results show that the algorithm has higher recognition rate, and it is feasible.关键词
动作识别/肢体角度模型/隐马尔可夫模型/条件限制玻尔兹曼兹曼机/深度置信网络Key words
action recognition/ limb angle model/ Hidden Markov Mode(HMM)/ Conditional Restricted Boltzman Machine (CRBM)/ deep belief network分类
信息技术与安全科学引用本文复制引用
杨世强,罗晓宇,李小莉,杨江涛,李德信..基于DBN-HMM的人体动作识别[J].计算机工程与应用,2019,55(15):169-176,8.基金项目
国家自然科学基金(No.51475365) （No.51475365）
陕西省教育厅省级重点实验室科学研究计划(No.12JS071) （No.12JS071）
陕西省自然科学基础研究计划(No.2017JM5088). （No.2017JM5088）
