info:eu-repo/semantics/other
Crowd Analysis with Deep Learning
Autor
Javier Gonzalez-Trejo
Institución
Resumen
This thesis tackles the problem of vision-based crowd analysis, with
one or multiple monocular cameras and using modern deep learning techniques.
To advance in the research of Crowd analysis applications, in this thesis, we
develop solutions to real problems based on our own lightweight Deep Learning
density map generator to solve recent challenges that involve the detection and
counting of crowds in monocular cameras. For that, first of all, identify the
state-of-the-art crowd counting and detection techniques, as well as the most
important public datasets that serve as the basis for this work and future developments.
More precisely, we will cover both, the development of a lightweight density map
generator for real-time embedded applications, and the development of crowd
analysis applications using state-of-the-art density map generators to solve two
interesting real-world problems: 1) Safe Landing Zones detection in populated
areas for Unmanned Aerial Vehicles (UAVs); and 2) Automatic Social Distance Monitoring.
The lightweight density map architecture was developed from a pruned density map
generator to reduce the number of parameters, and trained using the Bayesian
Loss to improve its accuracy in the crowd counting and detection tasks,
obtaining state-of-the-art Mean Square Error accuracy compared with the
literature, while maintaining a competitive number of parameters.
Using the lightweight density map generator, two crowd analysis solutions were
developed. More specifically, we developed a Safe Landing Zones detection and
tracking algorithm for UAVs emergency landing in populated scenarios, using the
lightweight density map algorithm embedded in a device with limited
computational resources, considering a camera mounted in the UAV. The density
map is used to generate an occupancy-free mask projected to the so-called head
plane, where the biggest circles free of people are found and tracked using
Kalman filters and the Hungarian algorithm for data association. The safe
landing detection algorithm was tested under real scenarios using data recorded
from a drone, showing promising results.
On the other hand, an Automatic Safe Distance Monitoring framework to train
Deep Neural Networks using density maps from non-social distance conforming
crowds was also developed in attention to the recent COVID-19 pandemic outbreak.
Based on public available crowds datasets, we propose a density map and a
segmentation-based solution, demonstrating superior perfo
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