Crowd evacuation simulation method combining the density field and social force model
Y Sun, H Liu - Physica A: Statistical Mechanics and its Applications, 2021 - Elsevier
Guiding crowd evacuation through changes in crowd density is one of the hot spots in crowd
simulation research. Crowd evacuation methods can intuitively guide crowds to avoid high …
simulation research. Crowd evacuation methods can intuitively guide crowds to avoid high …
Bayesian multi scale neural network for crowd counting
A Sagar - arxiv preprint arxiv:2007.14245, 2020 - arxiv.org
Crowd Counting is a difficult but important problem in computer vision. Convolutional Neural
Networks based on estimating the density map over the image has been highly successful in …
Networks based on estimating the density map over the image has been highly successful in …
Robust classification with noisy labels using Venn–Abers predictors
The advent of deep learning methods has led to impressive advances in computer vision
tasks over the past decades, largely due to their ability to extract non-linear features that are …
tasks over the past decades, largely due to their ability to extract non-linear features that are …
Crowd behavior characterization for scene tracking
In this work, we perform an in-depth analysis of the specific difficulties a crowded scene
dataset raises for tracking algorithms. Starting from the standard characteristics depicting the …
dataset raises for tracking algorithms. Starting from the standard characteristics depicting the …
[PDF][PDF] Estimation of Crowd Density from UAV Images based on Deep Learning
S Chauhan, AK Jha, K Mathur, TSP Sharma - academia.edu
Crowd monitoring is necessary to improve safety and controllable movements to minimize
risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event …
risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event …
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
The main objective of this work is to study the applicability of ensemble methods in the
context of deep learning with limited amounts of labeled data. We exploit an ensemble of …
context of deep learning with limited amounts of labeled data. We exploit an ensemble of …