基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法

人的行为识别是视频内容分析和计算机视觉领域中的一个重要问题. 在分析了人的行为包含多个尺度运动细节的基础上, 提出了一种分层且带驻留时间状态的动态贝叶斯网络(Hierarchical durational-state dynamic Bayesian network, HDS-DBN). HDS-DBN含有多层状态, 能够较好地表示人的行为包含的多尺度运动细节. 我们针对单人行为和两人交互行为进行了识别, 实验结果表明该方法具有较高的识别率, 并且在有噪声存在或信息缺失等不确定情况下均具有较好的鲁棒性. 实验结果表明 HDS-DBN 模型确实能够较好地表达行为中的多尺度运动细节.

Approach to Human Activity Multi-scale Analysis and Recognition Based on Multi-layer Dynamic Bayesian Network

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.