论文标题
使用密度图的无人机检测和监视密集的人群检测和监视
Dense Crowds Detection and Surveillance with Drones using Density Maps
论文作者
论文摘要
从移动的无人机中检测和计算人群中的人们提出的挑战性问题，这些问题是从图像的角度不断变化和卡米拉角度出现的。在本文中，我们测试了两种不同的最先进方法，即通过VGG19培训的贝叶斯损失功能的密度图生成，并用resnet50-fpn作为骨干进行检测，并检测到以骨干为骨架，以比较从无人机飞行中进行不同风景的人进行计数和检测。我们表明，当TheDrone在地面附近时，均提出的方法在稀疏人群中的探索和计数特别好。尽管如此，VGG19在这两个任务上都提供了更好的精神，同时也比fasterrcnn轻。此外，VGG19的表现更快地超过了rcnn，在密集的人群中，证明更强大的toscale变化和强烈的遮挡，更合适的使用无人机的寿司
Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using drones
