Approach to human activity multi-scale analysis and recognition based on multi-layer dynamic Bayesian network

被引：5

作者：

Du, You-Tian ^{[1
]}

Chen, Feng ^{[1
]}

Xu, Wen-Li ^{[1
]}

机构：

[1] Department of Automation, Tsinghua University

来源：

Zidonghua Xuebao/ Acta Automatica Sinica | 2009年 / 35卷 / 03期

关键词：

Computer vision; Dynamic Bayesian network; Human activity recognition; Video surveillance;

D O I：

10.3724/SP.J.1004.2009.00225

中图分类号：

学科分类号：

摘要：

Human activity recognition is an important issue in the fields of video content analysis and computer vision. Based on analyzing multiple scales of motion details contained in the human activities, we propose a novel human activity recognition approach named hierarchical durational-state dynamic Bayesian network (HDS-DBN). The HDS-DBN contains multiple levels of states and represents multiple scales of motion details as well. Experiments are conducted on the recognition of individual activities and two-person interacting activities. Experimental results show that the HDS-DBN recognizes human activities with high rates and has good robustness to the noise and loss of information. In addition, experimental results demonstrate that the HDS-DBN can represent multiple scales of motion details correctly. Copyright ©2009 Acta Automatica Sinica. All rights reserved.

引用

页码：225 / 232

页数：7

共 23 条

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