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A Target Detection Method in Dynamic Scene Based on Harris Algorithm with Sub-block Threshold

机译:基于哈里斯算法的动态场景在子块阈值下的目标检测方法

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In this paper, an improved Harris algorithm for the target detection in dynamic scene due to the camera motion is presented. First, a sub-block thresholding method is proposed to solve the problem of uneven distribution of corners detected by Harris algorithm. Then, the improved Harris algorithm is used to extract feature points, which are used to estimate the parameters of global motion with the random-max consistency algorithm and the least-square method. Compensated by the result parameters, the reference image together with the current image are used to detect the target with the frame difference method. Experiment results show that the algorithm can detect the moving target more accurately in dynamic scene.
机译:本文介绍了由于相机运动引起的动态场景中的目标检测的改进的Harris算法。首先,提出了一种子块阈值化方法来解决哈里斯算法检测到的角分布不均匀的问题。然后,改进的Harris算法用于提取特征点,该特征点用于估计随机最大一致性算法和最小二乘法的全局运动的参数。通过结果参数补偿,与当前图像一起的参考图像用于检测帧差法的目标。实验结果表明,该算法可以在动态场景中更准确地检测移动目标。

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