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基于高阶时空模型的视觉传感网络数据关联方法

万九卿 刘青云

万九卿, 刘青云. 基于高阶时空模型的视觉传感网络数据关联方法. 自动化学报, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
引用本文: 万九卿, 刘青云. 基于高阶时空模型的视觉传感网络数据关联方法. 自动化学报, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
WAN Jiu-Qing, LIU Qing-Yun. Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model. ACTA AUTOMATICA SINICA, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236
Citation: WAN Jiu-Qing, LIU Qing-Yun. Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model. ACTA AUTOMATICA SINICA, 2012, 38(2): 236-247. doi: 10.3724/SP.J.1004.2012.00236

基于高阶时空模型的视觉传感网络数据关联方法

doi: 10.3724/SP.J.1004.2012.00236
详细信息
    通讯作者:

    万九卿, 北京航空航天大学自动化学院讲师. 主要研究领域方向为信号/图像/视频处理, 统计推理与机器学习, 目标检测、跟踪与识别, 复杂系统故障诊断与健康管理. E-mail: wanjiuqing@gmail.com

Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model

  • 摘要: 数据关联是视觉传感网络联合监控系统的基本问题之一. 本文针对存在漏检条件下视觉传感网络的数据关联问题, 提出高阶时空观测模型并在此基础上建立了数据关联问题的动态贝叶斯网络描述. 给出了数据关联精确推理算法并分析了其计算复杂性, 接着根据不同的独立性假设提出两种近似推理算法以降低算法运算量, 并将提出的推理算法嵌入到EM算法框架中,使该算法能够应用于目标外观模型未知的情况. 仿真和实验结果表明了所提方法的有效性.
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出版历程
  • 收稿日期:  2010-11-24
  • 修回日期:  2011-05-15
  • 刊出日期:  2012-02-20

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