AIMC Topic: Crowding

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Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning.

PloS one Jul 2, 2025
In open environments, complex and variable backgrounds and dense multi-scale targets are two key challenges for crowd counting. Due to the reliance on supervised learning with labeled data, current methods struggle to adapt to crowd detection in comp...
Humans Algorithms Crowding Neural Networks, Computer Machine Learning
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Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

BMC emergency medicine Jan 6, 2025
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
Female Male Middle Aged Emergency Service, Hospital Crowding Humans France Algorithms Machine Learning Patient Admission Retrospective Studies
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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making Dec 18, 2024
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...
Humans Emergency Service, Hospital Crowding Machine Learning Forecasting
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Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.

Network (Bristol, England) Sep 20, 2024
Monitoring Surveillance video is really time-consuming, and the complexity of typical crowd behaviour in crowded situations makes this even more challenging. This has sparked a curiosity about computer vision-based anomaly detection. This study intro...
Crowding Deep Learning Video Recording Humans Algorithms
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Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

Health care management science Jun 10, 2024
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acu...
Humans Neural Networks, Computer Algorithms Computer Simulation Waiting Lists Italy Efficiency, Organizational Reproducibility of Results Crowding Patient Acuity Emergency Service, Hospital Resource Allocation
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A study on deep learning model based on global-local structure for crowd flow prediction.

Scientific reports Jun 1, 2024
Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for s...
Deep Learning Crowding COVID-19 SARS-CoV-2 Humans
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Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds.

Neural networks : the official journal of the International Neural Network Society Dec 20, 2023
Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To addre...
Reinforcement, Psychology Computer Simulation Crowding Deep Learning Motion
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Data-based tools can prevent crowd crushes.

Science (New York, N.Y.) Dec 8, 2022
Humans Artificial Intelligence Databases, Factual Mass Casualty Incidents Crowding
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Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

Sensors (Basel, Switzerland) Apr 22, 2022
In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context...
Awareness Neural Networks, Computer Crowding Image Processing, Computer-Assisted
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Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network.

Sensors (Basel, Switzerland) Dec 11, 2021
UNLABELLED: Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) came...
Crowding Neural Networks, Computer Humans Videotape Recording Recognition, Psychology
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