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首页> 外文期刊>Optical engineering >Moving target segmentation using Markov random field-based evaluation metric in infrared videos
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Moving target segmentation using Markov random field-based evaluation metric in infrared videos

机译：使用基于马尔可夫随机场的评估指标对红外视频进行运动目标分割

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摘要

A method for moving target detection and segmentation using Markov random field (MRF)-based evaluation metric in infrared videos has been proposed. Starting with the most useful seeds of a moving object, which are extracted based on the "holes" effect of temporal difference; the proposed method employs a region growing method using local gray information and a spatial and temporal MRF model-based evaluation metric without ground truth for moving target segmentation in infrared videos. The segmented mask of a moving target is grown from the most useful seeds using the region growing method with thresholds. The proposed evaluation metric is utilized to determine the best growing threshold, where the performance of moving target segmentation is measured by that of segmented mask's boundary. Thus, an MRF modeling for each boundary point of the segmented mask in spatial and temporal directions was considered by us. This problem is formulated using maximum a posteriori (MAP) estimation principle. At last, the global optimum of MRF-MAP framework is achieved using simulated annealing algorithm. The best segmented mask of a moving target is grown from the most useful seeds with the best growing threshold. Experimental results are reported to demonstrate the accuracy and robustness Of our algorithm.
机译：提出了一种基于马尔可夫随机场（MRF）的红外视频运动目标检测与分割方法。从运动对象最有用的种子开始，这些种子是根据时间差的“空洞”效应提取的；提出的方法采用区域增长方法，该方法使用局部灰度信息和基于空间和时间MRF模型的评估标准而没有地面真实性，用于红外视频中的运动目标分割。使用具有阈值的区域生长方法，从最有用的种子中生长出移动目标的分段蒙版。拟议的评估指标可用于确定最佳增长阈值，其中移动目标分割的性能由分割后的蒙版边界测量。因此，我们考虑了在空间和时间方向上对分段蒙版的每个边界点进行MRF建模。使用最大后验（MAP）估计原理来制定此问题。最后，采用模拟退火算法实现了MRF-MAP框架的全局最优。运动目标的最佳分割蒙版是从具有最佳生长阈值的最有用种子中生长出来的。据报道，实验结果证明了我们算法的准确性和鲁棒性。

著录项

  • 来源
    《Optical engineering》 |2018年第1期|013106.1-013106.15|共15页
  • 作者

    Bin Sun; Yingjie Li; Chen Guosheng; Junju Zhang; Bengkang Chang; Chaobo Min;

  • 作者单位

    Nanjing University of Posts and Telecommunications, School of Automation, Nanjing, Jiangsu Province, China;

    Nanjing University of Science and Technology, School of Electronic Engineering and Optic-Electronic Technology, Nanjing, Jiangsu Province, China;

    Northern Information Control Research Institute Group Co. Ltd., Nanjing, Jiangsu Province, China;

    Nanjing University of Science and Technology, School of Electronic Engineering and Optic-Electronic Technology, Nanjing, Jiangsu Province, China;

    Nanjing University of Science and Technology, School of Electronic Engineering and Optic-Electronic Technology, Nanjing, Jiangsu Province, China;

    Nanjing University of Science and Technology, School of Electronic Engineering and Optic-Electronic Technology, Nanjing, Jiangsu Province, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    moving target detection; spatiotemporal segmentation; Markov random field; performance evaluation; infrared video;

    机译：运动目标检测;时空分割马尔可夫随机场绩效评估;红外视频;

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