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Dim small moving target detection and tracking method based on spatial-temporal joint processing model

机译:基于空间关节加工模型的昏暗的小型移动目标检测与跟踪方法

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摘要

In order to solve the problem of weak target detection in complex moving background, a joint algorithm model of space-time domain is proposed in this paper. The algorithm first selects appropriate length of the time domain window and loop step size. In each time domain window: (1) In time domain processing stage, the algorithm fully considers the factors of background movement, and uses neighborhood similarity measure based on Pearson correlation coefficient to suppress most part of background area. (2) In space domain processing stage, a target detection method based on regional gray level (RGL) is proposed to suppress residual background and re-enhance the target. In each iteration, this joint detection model can obtain part of the trajectory of the target in real time. Therefore, after all iterations are completed (all images in the sequence have been processed), the target can be detected and the target trajectory can be efficiently extracted. The experimental results in this paper show that the proposed algorithm can effectively detect targets with maximum speed of 5 pixel/frame in the image sequence with average SCR <= 1 and background moving speed <= 0.8 pixel/frame.
机译:为了解决复杂移动背景中的弱目标检测问题,本文提出了一种时空域的联合算法模型。算法首先选择适当的时域窗口长度和循环步长。在每个时域窗口中:(1)在时域处理阶段,该算法完全考虑了背景移动的因素,并使用基于Pearson相关系数的邻域相似度量来抑制背景区域的大部分部分。 (2)在空间域处理阶段,提出了一种基于区域灰度(RGL)的目标检测方法来抑制残余背景并重新增强目标。在每次迭代中,该联合检测模型可以实时获得目标的部分轨迹。因此,在完成所有迭代(序列中的所有图像)之后,可以检测目标,并且可以有效地提取目标轨迹。本文的实验结果表明,该算法可以有效地检测具有在图像序列中具有5个像素/帧的最大速度的目标,其具有平均SCR <= 1和背景移动速度<= 0.8像素/帧。

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