A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds

Alia, Ahmed; Maree, Mohammed; Chraibi, Mohcine

doi:10.1109/AICCSA56895.2022.10017883

Contribution to a conference proceedings

FZJ-2022-06229

A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds

Alia, A. (Corresponding author)FZJ* ; Maree, M. ; Chraibi, M.FZJ*

2023
IEEE

2022 IEEE/ACS 19th International Conference on Computer Systems and Applications, AICCSA, Zayed University, Abu Dhabi, U Arab Emirates, 5 Dec 2022 - 7 Dec 2022 IEEE 1-2 (2023) [10.1109/AICCSA56895.2022.10017883]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33797 doi:10.1109/AICCSA56895.2022.10017883

Abstract: 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.

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