# Dense Crowds Detection and Surveillance with Drones using Density Maps

arXiv: https://arxiv.org/abs/2003.08766 · HTML: https://mlsanity.com/paper/2003.08766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting](https://mlsanity.com/paper/2202.03843/) (2022)
- [Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation](https://mlsanity.com/paper/2410.12988/) (2024)

## Abstract (arXiv)

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