Conference Presentation (After Call)

FZJ-2023-01262

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png DL4PuDe: Deep-Learning Framework for Pushing Detection in Pedestrian Dynamics

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

2023

Please use a persistent id in citations: http://hdl.handle.net/2128/34078

Abstract: 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.

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Contributing Institute(s):

1. Zivile Sicherheitsforschung (IAS-7)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. Pilotprojekt zur Entwicklung eines palästinensisch-deutschen Forschungs- und Promotionsprogramms 'Palestinian-German Science Bridge' (01DH16027) (01DH16027)

Appears in the scientific report 2023

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Database coverage:

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