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Improved Pairwise Max Suppression Considering Total Number of Targets

机译:考虑到总目标的总数改善成对的最大抑制

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The authors try to construct a novel sensor networking scheme that obtains the location of sensor nodes using image processing in order to enable dynamic routing when the speed and the density of the sensor nodes become high. In this scheme, visual object detection is applied for the localization of sensor nodes; however, certain failures occur during the region merging process, which is required for schemes based on sliding windows. For the widely used pairwise max suppression(PMS), the most significant problem is the fixed threshold for region merging. To solve this problem, this paper proposes a novel scheme for region merging that adopts adaptive thresholding for the existing PMS. The threshold is appropriately determined by taking into account the total number of detection targets. The experimental results, conducted using a dataset composed of top-view images generated from a CG-based virtual space, showed that the miss rate can be reduced to approximately 67.4% of the existing PMS.
机译:作者尝试构建一种新颖的传感器网络方案,该方案使用图像处理获得传感器节点的位置,以便在传感器节点的速度和密度变高时启用动态路由。在该方案中,应用Visual对象检测用于定位传感器节点;然而,某些故障发生在区域合并过程中,这是基于滑动窗口的方案所必需的。对于广泛使用的成对最大抑制(PMS),最重要的问题是区域合并的固定阈值。为了解决这个问题,本文提出了一种新的区域合并的新方案,用于对现有PMS采用自适应阈值的影响。通过考虑检测目标的总数来适当地确定阈值。使用由基于CG的虚拟空间产生的顶视图组成的数据集进行的实验结果表明,未命中率可以减少到现有PM的大约67.4%。

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