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| Hauptseite > Publikationsdatenbank > Artificial Intelligence Framework for Video Analytics: Detecting Pushing in Crowds > print |
| Hauptseite > Publikationsdatenbank > Artificial Intelligence Framework for Video Analytics: Detecting Pushing in Crowds > print |
| 001 | 1028429 | ||
| 005 | 20240716202035.0 | ||
| 020 | _ | _ | |a 978-3-95806-763-9 |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-04610 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-04610 |
| 100 | 1 | _ | |a Alia, Ahmed |0 P:(DE-Juel1)185971 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Artificial Intelligence Framework for Video Analytics: Detecting Pushing in Crowds |f - 2024 |
| 260 | _ | _ | |a Jülich |c 2024 |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |
| 300 | _ | _ | |a xviii, 151 |
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| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich IAS Series |v 61 |
| 502 | _ | _ | |a Dissertation, Univ. Wuppertal, 2024 |c Univ. Wuppertal |b Dissertation |d 2024 |
| 520 | _ | _ | |a In the modern era, data has become more complex, posing additional challenges to conventional data analysis methods. This is where Artificial Intelligence comes into play, specifically Deep Learning algorithms. These algorithms can analyze such data automatically, quickly, and accurately. Moreover, they can explore complex relationships between variables and identify non-linear patterns humans may not perceive. Leveraging this potential, Deep Learning has recently become pivotal in analyzing complex data, such as video data, arising from human crowds to enhance safety. Despite considerable advancements, some challenging problems in crowd dynamics still need to be solved efficiently and automatically. |
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