The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.

中文翻译：

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深度学习技术在高密度条件下自动检测行人

对于计算机视觉领域和行人流量研究，在高密度条件下自动检测和跟踪行人是一项艰巨的任务。收集行人数据是人群管理的建模和实际实现的基本任务。尽管有许多检测行人的方法，但在高密度情况下可能不容易采用它们。因此，我们利用了一种基于深度学习算法的新兴方法。根据无人驾驶飞机（UAV）记录的一些行人流量实验的俯视视频数据，我们产生了自己的训练数据集。我们通过使用Yolo v3训练检测模型，Yolo v3是近年来许多可用的检测模型中非常流行的深度学习模型。我们发现检测结果很好；例如精度，召回率，。我们认为这种方法可用于其他具有相似配置的行人流量实验或现场数据，也可用于自动人群密度估计。