基于GHT的多目标检测自适应终止算法
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陕西广播电视大学信息与智能技术学院,西安,710119

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陕西省教育科学规划课题(SGH16V022)资助项目;陕西广播电视大学2017年度科研重点课题(17D-08-A06)资助项目。


Multi-target Detection Adaptive Termination Algorithm Based on GHT
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School of Information and Intelligence Technology, Shaanxi Radio & TV University, Xi’an, 710119, China

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    摘要:

    针对广义霍夫变换(Generalized Hough transform,GHT)在多目标检测时存在的难以自适应终止的问题,提出基于霍夫空间局部峰值变化率的GHT多目标检测自适应终止算法。该算法主要依据待检测图像的霍夫空间中目标区域的局部峰值之间差异较小,而有目标区域与无目标区域的局部峰值之间差异较大的规律,使得GHT类算法可以自适应终止。该算法的主要步骤为:首先通过GHT算法获得目标在原始图像中的累计匹配分布,并对分布结果进行降序排序,再根据累计峰值的平均变化率自适应地检测出多个目标识别结果,并终止算法。实验表明,相对于传统算法,该算法在没有明显增加算法复杂度的情况下,能够准确地检测出图像中存在的多目标信息,并且能够实现多目标检测算法的自适应终止。

    Abstract:

    Aiming at the problem that the Generalized Hough transform (GHT) is difficult to adaptively terminate in multi-target detection, an adaptive termination algorithm for GHT multi-target detection based on the local peak rate of change in Hough space is proposed. The algorithm is mainly based on the rule that the difference between the local peaks in the target region in the Hough space of the image for detecting is small, and the difference between the local peaks in the target region and the non-target region is big, which leads to the GHT algorithm terminating adaptively without setting a threshold. And the main steps of the algorithm are as follows: Firstly, the cumulative matching distribution of the target in the original image is obtained by the GHT algorithm. Then the distribution results are sorted in descending order. After the sorted distribution is obtained, multiple target recognition results are adaptively detected according to the average change rate of the accumulated peaks, and the algorithm is terminated. Experiments show that compared with the traditional algorithm, this algorithm can accurately detect the multi-target information of the image without significantly increasing the complexity of the algorithm, and can realize the adaptive termination of the multi-target detection algorithm.

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杨思燕,贺国旗.基于GHT的多目标检测自适应终止算法[J].数据采集与处理,2020,35(3):526-535

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  • 收稿日期:2020-01-20
  • 最后修改日期:2020-02-29
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  • 在线发布日期: 2020-05-25