Alessandro Corbetta

Orcid: 0000-0001-6979-3414

According to our database1, Alessandro Corbetta authored at least 15 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2023
How neural networks learn to classify chaotic time series.
CoRR, 2023

2022
Towards a Numerical Proof of Turbulence Closure.
CoRR, 2022

Towards Sound-based Crowd Management: Investigating Sonification for Pedestrian Steering.
Proceedings of the AM '22: AudioMostly 2022, St. Pölten, 2022

2021
Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization.
J. Comput. Sci., 2021

Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control.
CoRR, 2021

2020
Controlling Rayleigh-Bénard convection via Reinforcement Learning.
CoRR, 2020

Pedestrian orientation dynamics from high-fidelity measurements.
CoRR, 2020

2019
Measurement and analysis of visitors' trajectories in crowded museums.
CoRR, 2019

Deep learning velocity signals allows to quantify turbulence intensity.
CoRR, 2019

Stampnet: Unsupervised Multi-Class Object Discovery.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

2018
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG.
CoRR, 2018

2017
TurBase: A Software Platform for Research in Experimental and Numerical Fluid Dynamics.
Proceedings of the 2017 International Conference on High Performance Computing & Simulation, 2017

Depth driven people counting using deep region proposal network.
Proceedings of the IEEE International Conference on Information and Automation, 2017

Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields.
Proceedings of the 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2017

2014
Parameter Estimation of Social Forces in Crowd Dynamics Models via a Probabilistic Method.
CoRR, 2014


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