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首页> 外文期刊>Journal of Field Robotics >The nested k-means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles
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The nested k-means method: A new approach for detecting lost persons in aerial images acquired by unmanned aerial vehicles

机译：嵌套k均值方法：一种检测无人驾驶飞机获取的空中图像中迷路人员的新方法

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摘要

A new method, named as the nested k-means, for detecting a person captured in aerial images acquired by an unmanned aerial vehicle (UAV), is presented. The nested k-means method is used in a newly built system that supports search and rescue (SAR) activities through processing of aerial photographs taken in visible light spectra (red-green-blue channels, RGB). First, the k-means classification is utilized to identify clusters of colors in a three-dimensional space (RGB). Second, the k-means method is used to verify if the automatically selected class of colors is concurrently spatially clustered in a two-dimensional space (easting-northing, EN), and has human-size area. The UAV images were acquired during the field campaign carried out in the Izerskie Mountains (SW Poland). The experiment aimed to observe several persons using an RGB camera, in spring and winter, during various periods of day, in uncovered terrain and sparse forest. It was found that the nested k-means method has a considerable potential for detecting a person lost in the wilderness and allows to reduce area to be searched to 4.4 and 7.3% in spring and winter, respectively. In winter, land cover influences the performance of the nested k-means method, with better skills in sparse forest than in the uncovered terrain. In spring, such a relationship does not hold. The nested k-means method may provide the SAR teams with a tool for near real-time detection of a person and, as a consequence, to reduce search area to approximately 0.5-7.3% of total terrain to be visited, depending on season and land cover.
机译：提出了一种称为嵌套k均值的新方法，该方法用于检测在由无人机（UAV）采集的空中图像中捕获的人。嵌套k均值方法用于新建的系统，该系统通过处理在可见光谱（红绿蓝通道，RGB）中拍摄的航拍照片来支持搜索和救援（SAR）活动。首先，使用k均值分类来识别三维空间（RGB）中的颜色簇。其次，k均值方法用于验证自动选择的颜色类别是否同时在二维空间（东北，EN）中进行空间聚类，并具有与人一样大的区域。无人机图像是在Izerskie山（波兰西南部）进行的野战中获取的。该实验的目的是在春季和冬季的白天，不同时段，在未被覆盖的地形和稀疏森林中使用RGB相机观察几个人。已发现，嵌套k均值法具有检测在荒野中迷路的人的巨大潜力，并且可以将春季和冬季的搜索面积分别减小到4.4％和7.3％。在冬季，土地覆盖会影响嵌套k均值方法的性能，在稀疏森林中比在未覆盖的地形中具有更好的技能。在春天，这种关系不成立。嵌套k均值方法可以为SAR团队提供一种用于近实时检测人员的工具，从而根据季节和条件，将搜索区域缩小到要访问的总地形的大约0.5-7.3％。土地覆盖。

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