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首页> 外文期刊>Journal of Imaging Science and Technology >Robustly Adaptive Moving Thermal Object Segmentation Using Background Modeling Based on Runtime-Weighted Features
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Robustly Adaptive Moving Thermal Object Segmentation Using Background Modeling Based on Runtime-Weighted Features

机译：基于运行时加权特征的背景建模鲁棒自适应移动热目标分割

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摘要

Moving object segmentation plays an important role in a complex object tracking system. This system decides whether the current block belongs to the object region or not. In this article, a scheme using background modeling based on runtime-weighted features for robustly adaptive moving object segmentation in infrared (IR) image sequence is proposed. Proposed background modeling for an open hardware (H/W) architecture design decreases the size of the search area to construct a sparse block template of search area in infrared images. The authors also compensate for motion compensation when the image moves in previous and current frames of IR imaging system. The method of separation of background and objects applies to adaptive values through time analysis of pixel intensity. The proposed method uses more feature information such as intensity, deviation, block matching error, and velocity. The weighting values give a higher weight to feature information which has a large difference between the object and the background region. Based on experimental results, the proposed method showed real-time moving object segmentation through background modeling in the proposed embedded system.
机译：运动对象分割在复杂的对象跟踪系统中起着重要的作用。该系统确定当前块是否属于对象区域。在本文中，提出了一种基于背景模型的基于运行时加权特征的方案，用于在红外（IR）图像序列中进行鲁棒的自适应运动对象分割。对于开放硬件（H / W）体系结构设计的拟议背景建模可减小搜索区域的大小，以构造红外图像中搜索区域的稀疏块模板。当图像在红外成像系统的前一帧和当前帧中移动时，作者还可以补偿运动补偿。通过对像素强度进行时间分析，背景和对象的分离方法适用于自适应值。所提出的方法使用更多的特征信息，例如强度，偏差，块匹配误差和速度。权重值赋予对象与背景区域之间的差异较大的特征信息较高的权重。基于实验结果，该方法通过背景建模在嵌入式系统中实现了实时的运动目标分割。

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  • 来源
    《Journal of Imaging Science and Technology》 |2010年第2期|P.020505.1-020505.9|共9页
  • 作者

    Changhan Park; Jik-Han Jung; Kyung-Hoon Bae;

  • 作者单位

    SIAT, Samsung Thales Co., Ltd., Chang-Li 304, Namsa-myun, Cheoin-gu, Yongin City, 449-885 Gyeonggi-do, South Korea;

    rnSchool of EECS, KAIST, 373-1, Guseong-Dong, Yuseong-Gu, 305-701 Daejeon, South Korea;

    rnSIAT, Samsung Thales Co., Ltd., Chang-Li 304, Namsa-myun, Cheoin-gu, Yongin City, 449-885 Gyeonggi-do, South Korea;

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