%0 Conference Paper
%A Alia, Ahmed
%A Maree, Mohammed
%A Chraibi, Mohcine
%T A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds
%I IEEE
%M FZJ-2022-06229
%P 1-2
%D 2023
%X Deep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data. As a result, deep learning is widely applied in the human crowd analysis domain. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.
%B 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications
%C 5 Dec 2022 - 7 Dec 2022, Zayed University, Abu Dhabi (U Arab Emirates)
Y2 5 Dec 2022 - 7 Dec 2022
M2 Zayed University, Abu Dhabi, U Arab Emirates
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U <Go to ISI:>//WOS:000932894200052
%R 10.1109/AICCSA56895.2022.10017883
%U https://juser.fz-juelich.de/record/916435