Abstract
摘要: 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. 关键词: 人的行为识别 / 计算机视觉 / 视频监控 / 动态贝叶斯网络
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