Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

243
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
243

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.3K

Measuring Dynamics in Evacuation Behaviour with Deep Learning.

Huaidian Hou1, Lingxiao Wang2,3

  • 1The Haverford School, 450 Lancaster Avenue, Haverford, PA 19010, USA.

Entropy (Basel, Switzerland)
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study uses deep learning to predict crowd evacuation dynamics from images. The framework extracts a key parameter reflecting optimal path choices, even with incomplete data.

Keywords:
crowd behaviourdeep learningevacuation

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Related Experiment Videos

Last Updated: Oct 2, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.3K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Area of Science:

  • Computational Social Science
  • Artificial Intelligence

Background:

  • Human evacuation behavior is influenced by bounded rationality and heterogeneous information, leading to suboptimal choices.
  • Collective evacuation dynamics are complex and challenging to model accurately.

Purpose of the Study:

  • To develop a deep learning framework for extracting key dynamical parameters from crowd evacuation simulations.
  • To quantitatively measure the optimality of path-choosing decisions in evacuation behavior.

Main Methods:

  • Utilized deep convolution neural networks (CNNs) trained on simulation data from a replica dynamic cellular automaton (CA) model.
  • Applied the framework to predict crowd dynamics from multiple image frames, assessing robustness to incomplete data.

Main Results:

  • Trained CNNs accurately predicted crowd evacuation dynamics.
  • Successfully extracted a dynamical parameter quantifying the optimality of path-choosing decisions.
  • Demonstrated the method's robustness to incomplete image data.

Conclusions:

  • The proposed deep learning framework effectively extracts crucial parameters governing crowd evacuation.
  • This approach offers a quantitative method for evaluating evacuation strategies and can be extended to other crowd behavior experiments.