4 research outputs found
Dense Crowds Detection and Surveillance with Drones using Density Maps
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 dronesComment: 2020 International Conference on Unmanned Aircraft Systems (ICUAS),
Athens, Greece, 202