AIMC Topic: Crowding

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Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning.

IEEE transactions on cybernetics
Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human-robot interaction (...

Collaborative Active Visual Recognition from Crowds: A Distributed Ensemble Approach.

IEEE transactions on pattern analysis and machine intelligence
Active learning is an effective way of engaging users to interactively train models for visual recognition more efficiently. The vast majority of previous works focused on active learning with a single human oracle. The problem of active learning wit...

An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

Computational intelligence and neuroscience
The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptan...

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performa...

Online anomaly detection in crowd scenes via structure analysis.

IEEE transactions on cybernetics
Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural contex...

Does surface completion fail to support uncrowding?

Journal of vision
In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local p...

Crowd Counting Using Meta-Test-Time Adaptation.

International journal of neural systems
Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the s...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Studies in health technology and informatics
Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem h...

Crowding and attention in a framework of neural network model.

Journal of vision
In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, ...