TY  - THES
AU  - Alia, Ahmed
TI  - Artificial Intelligence Framework for Video Analytics: Detecting Pushing in Crowds
VL  - 61
PB  - Univ. Wuppertal
VL  - Dissertation
CY  - Jülich
M1  - FZJ-2024-04610
SN  - 978-3-95806-763-9
T2  - Schriften des Forschungszentrums Jülich IAS Series
SP  - xviii, 151
PY  - 2024
N1  - Dissertation, Univ. Wuppertal, 2024
AB  - 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.
LB  - PUB:(DE-HGF)3 ; PUB:(DE-HGF)11
DO  - DOI:10.34734/FZJ-2024-04610
UR  - https://juser.fz-juelich.de/record/1028429
ER  -