TY  - CONF
AU  - Alia, Ahmed
AU  - Maree, Mohammed
AU  - Chraibi, Mohcine
TI  - A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds
PB  - IEEE
M1  - FZJ-2022-06229
SP  - 1-2
PY  - 2023
AB  - 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.
T2  - 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications
CY  - 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
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:000932894200052
DO  - DOI:10.1109/AICCSA56895.2022.10017883
UR  - https://juser.fz-juelich.de/record/916435
ER  -