TY  - CONF
AU  - Alia, Ahmed
AU  - Maree, Mohammed
AU  - Chraibi, Mohcine
TI  - DL4PuDe: Deep-Learning Framework for Pushing Detection in Pedestrian Dynamics
M1  - FZJ-2023-01262
PY  - 2023
AB  - At crowded event entrances, some pedestrians start pushing others to gain faster access to the events, resulting in dangerous situations. Pushing identification in video recordings of events is crucial for understanding pushing dynamics, thereby managing entrances safely. This talk presents a deep-learning framework to help researchers automatically identify pushing in videos of crowds. The framework consists of four modules: (1) Optical Flow Estimator that uses a pre-trained optical flow model to estimate the dense displacement fields from input video. (2) Wheel Visualization for generating motion information maps from the displacement fields. (3) EfficientNet-B0 Classifier that aims to identify pushing behavior from the maps. (4) A False Reduction and Annotation module; to reduce the number of false identifications of the classifier, annotate the regions of pushing and output the annotated video. We used five real-world ground truth of pushing behavior videos for the evaluation. Experimental results show that the framework achieves 86% accuracy. The framework is open-source and available at https://github.com/PedestrianDynamics/DL4PuDe.
T2  - Conference for Research Software Engineering in Germany
CY  - 20 Feb 2023 - 22 Feb 2023, Paderborn (Germany)
Y2  - 20 Feb 2023 - 22 Feb 2023
M2  - Paderborn, Germany
LB  - PUB:(DE-HGF)6
UR  - https://juser.fz-juelich.de/record/1003812
ER  -